Hongwei Yu1, Xianqi Meng2, Huang Chen3, Jian Liu4, Wenwen Gao1,5, Lei Du1,6, Yue Chen1,5, Yige Wang1,6, Xiuxiu Liu1,5, Bing Liu1,6, Jingfan Fan2, Guolin Ma1. 1. Department of Radiology, China-Japan Friendship Hospital, Beijing, China. 2. Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Photonics, Beijing Institute of Technology, Beijing, China. 3. Department of Pathology, China-Japan Friendship Hospital, Beijing, China. 4. Department of Ultrasound medicine, China-Japan Friendship Hospital, Beijing, China. 5. Peking University China-Japan Friendship School of Clinical Medicine, Beijing, China. 6. Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Abstract
OBJECTIVES: This study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer. METHODS: Data from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann-Whitney U test to evaluate the level of TILs in the low and high groups. RESULTS: Among the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738-0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615-0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively). CONCLUSION: Radiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.
OBJECTIVES: This study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer. METHODS: Data from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann-Whitney U test to evaluate the level of TILs in the low and high groups. RESULTS: Among the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738-0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615-0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively). CONCLUSION: Radiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.
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: R Salgado; C Denkert; S Demaria; N Sirtaine; F Klauschen; G Pruneri; S Wienert; G Van den Eynden; F L Baehner; F Penault-Llorca; E A Perez; E A Thompson; W F Symmans; A L Richardson; J Brock; C Criscitiello; H Bailey; M Ignatiadis; G Floris; J Sparano; Z Kos; T Nielsen; D L Rimm; K H Allison; J S Reis-Filho; S Loibl; C Sotiriou; G Viale; S Badve; S Adams; K Willard-Gallo; S Loi Journal: Ann Oncol Date: 2014-09-11 Impact factor: 32.976
Authors: Hyunjin Park; Yaeji Lim; Eun Sook Ko; Hwan-Ho Cho; Jeong Eon Lee; Boo-Kyung Han; Eun Young Ko; Ji Soo Choi; Ko Woon Park Journal: Clin Cancer Res Date: 2018-06-18 Impact factor: 12.531
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