Richard Ha1, Peter Chang2, Simukayi Mutasa2, Jenika Karcich2, Sarah Goodman2, Elyse Blum2, Kevin Kalinsky3, Michael Z Liu4, Sachin Jambawalikar4. 1. Breast Imaging Section, Department of Radiology, Columbia University Medical Center, New York, New York, USA. 2. Department of Radiology, Columbia University Medical Center, New York, New York, USA. 3. Division of Hematology/Oncology in the Department of Medicine at Columbia University Medical Center, New York, New York, USA. 4. Department of Medical Physics, Columbia University Medical Center, New York, New York, USA.
Abstract
BACKGROUND: Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet the criteria of estrogen receptor positive / human epidermal growth factor receptor-2 negative (ER+/HER2-)/node negative invasive breast carcinoma. Although effective, the test is invasive and expensive, which has motivated this investigation to determine the potential role of radiomics. HYPOTHESIS: We hypothesized that convolutional neural network (CNN) can be used to predict Oncotype Dx RS using an MRI dataset. STUDY TYPE: Institutional Review Board (IRB)-approved retrospective study from January 2010 to June 2016. POPULATION: In all, 134 patients with ER+/HER2- invasive ductal carcinoma who underwent both breast MRI and Oncotype Dx RS evaluation. Patients were classified into three groups: low risk (group 1, RS <18), intermediate risk (group 2, RS 18-30), and high risk (group 3, RS >30). FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T. Breast MRI, T1 postcontrast. ASSESSMENT: Each breast tumor underwent 3D segmentation. In all, 1649 volumetric slices in 134 tumors (mean 12.3 slices/tumor) were evaluated. A CNN consisted of four convolutional layers and max-pooling layers. Dropout at 50% was applied to the second to last fully connected layer to prevent overfitting. Three-class prediction (group 1 vs. group 2 vs. group 3) and two-class prediction (group 1 vs. group 2/3) models were performed. STATISTICAL TESTS: A 5-fold crossvalidation test was performed using 80% training and 20% testing. Diagnostic accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) area under the curve (AUC) were evaluated. RESULTS: The CNN achieved an overall accuracy of 81% (95% confidence interval [CI] ± 4%) in three-class prediction with specificity 90% (95% CI ± 5%), sensitivity 60% (95% CI ± 6%), and the area under the ROC curve was 0.92 (SD, 0.01). The CNN achieved an overall accuracy of 84% (95% CI ± 5%) in two-class prediction with specificity 81% (95% CI ± 4%), sensitivity 87% (95% CI ± 5%), and the area under the ROC curve was 0.92 (SD, 0.01). DATA CONCLUSION: It is feasible for current deep CNN architecture to be trained to predict Oncotype DX RS. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:518-524.
BACKGROUND: Oncotype Dx is a validated genetic analysis that provides a recurrence score (RS) to quantitatively predict outcomes in patients who meet the criteria of estrogen receptor positive / humanepidermal growth factor receptor-2 negative (ER+/HER2-)/node negative invasive breast carcinoma. Although effective, the test is invasive and expensive, which has motivated this investigation to determine the potential role of radiomics. HYPOTHESIS: We hypothesized that convolutional neural network (CNN) can be used to predict Oncotype Dx RS using an MRI dataset. STUDY TYPE: Institutional Review Board (IRB)-approved retrospective study from January 2010 to June 2016. POPULATION: In all, 134 patients with ER+/HER2- invasive ductal carcinoma who underwent both breast MRI and Oncotype Dx RS evaluation. Patients were classified into three groups: low risk (group 1, RS <18), intermediate risk (group 2, RS 18-30), and high risk (group 3, RS >30). FIELD STRENGTH/SEQUENCE: 1.5T and 3.0T. Breast MRI, T1 postcontrast. ASSESSMENT: Each breast tumor underwent 3D segmentation. In all, 1649 volumetric slices in 134 tumors (mean 12.3 slices/tumor) were evaluated. A CNN consisted of four convolutional layers and max-pooling layers. Dropout at 50% was applied to the second to last fully connected layer to prevent overfitting. Three-class prediction (group 1 vs. group 2 vs. group 3) and two-class prediction (group 1 vs. group 2/3) models were performed. STATISTICAL TESTS: A 5-fold crossvalidation test was performed using 80% training and 20% testing. Diagnostic accuracy, sensitivity, specificity, and receiver operating characteristic (ROC) area under the curve (AUC) were evaluated. RESULTS: The CNN achieved an overall accuracy of 81% (95% confidence interval [CI] ± 4%) in three-class prediction with specificity 90% (95% CI ± 5%), sensitivity 60% (95% CI ± 6%), and the area under the ROC curve was 0.92 (SD, 0.01). The CNN achieved an overall accuracy of 84% (95% CI ± 5%) in two-class prediction with specificity 81% (95% CI ± 4%), sensitivity 87% (95% CI ± 5%), and the area under the ROC curve was 0.92 (SD, 0.01). DATA CONCLUSION: It is feasible for current deep CNN architecture to be trained to predict Oncotype DX RS. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:518-524.
Authors: E Senkus; S Kyriakides; S Ohno; F Penault-Llorca; P Poortmans; E Rutgers; S Zackrisson; F Cardoso Journal: Ann Oncol Date: 2015-09 Impact factor: 32.976
Authors: Soonmyung Paik; Gong Tang; Steven Shak; Chungyeul Kim; Joffre Baker; Wanseop Kim; Maureen Cronin; Frederick L Baehner; Drew Watson; John Bryant; Joseph P Costantino; Charles E Geyer; D Lawrence Wickerham; Norman Wolmark Journal: J Clin Oncol Date: 2006-05-23 Impact factor: 44.544
Authors: Soonmyung Paik; Steven Shak; Gong Tang; Chungyeul Kim; Joffre Baker; Maureen Cronin; Frederick L Baehner; Michael G Walker; Drew Watson; Taesung Park; William Hiller; Edwin R Fisher; D Lawrence Wickerham; John Bryant; Norman Wolmark Journal: N Engl J Med Date: 2004-12-10 Impact factor: 91.245
Authors: Elizabeth J Sutton; Jung Hun Oh; Brittany Z Dashevsky; Harini Veeraraghavan; Aditya P Apte; Sunitha B Thakur; Joseph O Deasy; Elizabeth A Morris Journal: J Magn Reson Imaging Date: 2015-04-07 Impact factor: 4.813
Authors: C M Perou; T Sørlie; M B Eisen; M van de Rijn; S S Jeffrey; C A Rees; J R Pollack; D T Ross; H Johnsen; L A Akslen; O Fluge; A Pergamenschikov; C Williams; S X Zhu; P E Lønning; A L Børresen-Dale; P O Brown; D Botstein Journal: Nature Date: 2000-08-17 Impact factor: 49.962
Authors: Hongxiao Li; Jigang Wang; Zaibo Li; Melad Dababneh; Fusheng Wang; Peng Zhao; Geoffrey H Smith; George Teodoro; Meijie Li; Jun Kong; Xiaoxian Li Journal: Front Med (Lausanne) Date: 2022-06-14
Authors: Austin D Williams; Kate R Pawloski; Hannah Y Wen; Varadan Sevilimedu; Donna Thompson; Monica Morrow; Mahmoud El-Tamer Journal: Breast Cancer Res Treat Date: 2022-10-21 Impact factor: 4.624
Authors: Shinn-Huey S Chou; Justin Romanoff; Constance D Lehman; Seema A Khan; Ruth Carlos; Sunil S Badve; Jennifer Xiao; Ralph L Corsetti; Sara H Javid; Derrick W Spell; Linda K Han; Jennifer L Sabol; John R Bumberry; Ilana F Gareen; Bradley S Snyder; Constantine Gatsonis; Lynne I Wagner; Antonio C Wolff; Kathy D Miller; Joseph A Sparano; Christopher E Comstock; Habib Rahbar Journal: Radiology Date: 2021-08-03 Impact factor: 29.146