Jie Ding1, Shenglan Chen2, Mario Serrano Sosa2, Renee Cattell2, Lan Lei3, Junqi Sun4, Prateek Prasanna5, Chunling Liu6, Chuan Huang7. 1. Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA; Department of Radiation Oncology, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Wauwatosa, WI 53226, USA. 2. Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA. 3. Pogram in Program in Public Health, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA. 4. Department of Radiology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA; Department of Radiology, Yuebei People's Hospital, 133 Huimin S Rd, Shaoguan, Guangdong 512025, China. 5. Department of Biomedical Informatics, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA. 6. Department of Radiology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA; Department of Radiology, Guangdong General Hospital/Guangdong Academy of Medical Sciences, 106 Zhongshan 2nd Rd, Guangzhou, Guangdong 510080, China. 7. Department of Biomedical Engineering, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY 11794, USA; Department of Radiology, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA; Department of Psychiatry, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY 11794, USA. Electronic address: chuan.huang@stonybrookmedicine.edu.
Abstract
RATIONALE AND OBJECTIVES: Peritumoral features have been suggested to be useful in improving the prediction performance of radiomic models. The aim of this study is to systematically investigate the prediction performance improvement for sentinel lymph node (SLN) status in breast cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral region sizes. MATERIALS AND METHODS: This retrospective study was performed using dynamic contrast-enhanced MRI scans of 162 breast cancer patients. The effect of peritumoral features was evaluated in a radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions were generated by dilating the tumor regions-of-interest (ROIs) manually annotated by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction models were established in the training set (∼67% of cases) using the radiomics pipeline with and without peritumoral features derived from different peritumoral thicknesses. The prediction performance was tested in an independent validation set (the remaining ∼33%). RESULTS: For this specific application, the accuracy in the validation set when using the two radiologists' ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral features. The choice of the peritumoral size could affect the level of improvement. CONCLUSION: This study systematically investigates the effect of peritumoral region sizes in radiomic analysis for prediction performance improvement. The choice of the peritumoral size is dependent on the ROI drawing and would affect the final prediction performance of radiomic models, suggesting that peritumoral features should be optimized in future radiomics studies.
RATIONALE AND OBJECTIVES: Peritumoral features have been suggested to be useful in improving the prediction performance of radiomic models. The aim of this study is to systematically investigate the prediction performance improvement for sentinel lymph node (SLN) status in breast cancer from peritumoral features in radiomic analysis by exploring the effect of peritumoral region sizes. MATERIALS AND METHODS: This retrospective study was performed using dynamic contrast-enhanced MRI scans of 162 breast cancer patients. The effect of peritumoral features was evaluated in a radiomics pipeline for predicting SLN metastasis in breast cancer. Peritumoral regions were generated by dilating the tumor regions-of-interest (ROIs) manually annotated by two expert radiologists, with thicknesses of 2 mm, 4 mm, 6 mm, and 8 mm. The prediction models were established in the training set (∼67% of cases) using the radiomics pipeline with and without peritumoral features derived from different peritumoral thicknesses. The prediction performance was tested in an independent validation set (the remaining ∼33%). RESULTS: For this specific application, the accuracy in the validation set when using the two radiologists' ROIs could be both improved from 0.704 to 0.796 by incorporating peritumoral features. The choice of the peritumoral size could affect the level of improvement. CONCLUSION: This study systematically investigates the effect of peritumoral region sizes in radiomic analysis for prediction performance improvement. The choice of the peritumoral size is dependent on the ROI drawing and would affect the final prediction performance of radiomic models, suggesting that peritumoral features should be optimized in future radiomics studies.
Authors: Sebastian Bickelhaupt; Daniel Paech; Philipp Kickingereder; Franziska Steudle; Wolfgang Lederer; Heidi Daniel; Michael Götz; Nils Gählert; Diana Tichy; Manuel Wiesenfarth; Frederik B Laun; Klaus H Maier-Hein; Heinz-Peter Schlemmer; David Bonekamp Journal: J Magn Reson Imaging Date: 2017-02-02 Impact factor: 4.813
Authors: K Schoenegger; S Oberndorfer; B Wuschitz; W Struhal; J Hainfellner; D Prayer; H Heinzl; H Lahrmann; C Marosi; W Grisold Journal: Eur J Neurol Date: 2009-04-14 Impact factor: 6.089
Authors: Hui Li; Yitan Zhu; Elizabeth S Burnside; Erich Huang; Karen Drukker; Katherine A Hoadley; Cheng Fan; Suzanne D Conzen; Margarita Zuley; Jose M Net; Elizabeth Sutton; Gary J Whitman; Elizabeth Morris; Charles M Perou; Yuan Ji; Maryellen L Giger Journal: NPJ Breast Cancer Date: 2016-05-11
Authors: Nathaniel M Braman; Maryam Etesami; Prateek Prasanna; Christina Dubchuk; Hannah Gilmore; Pallavi Tiwari; Donna Plecha; Anant Madabhushi Journal: Breast Cancer Res Date: 2017-05-18 Impact factor: 6.466