Literature DB >> 28675570

Predicting the level of tumor-infiltrating lymphocytes in patients with triple-negative breast cancer: Usefulness of breast MRI computer-aided detection and diagnosis.

You Jin Ku1, Hak Hee Kim2, Joo Hee Cha2, Hee Jung Shin2, Eun Young Chae2, Woo Jung Choi2, Hee Jin Lee3, Gyungyub Gong3.   

Abstract

PURPOSE: To evaluate the usefulness of magnetic resonance imaging (MRI) computer-aided detection and diagnosis (CAD) for the detection of tumor-infiltrating lymphocytes (TILs) in triple-negative breast cancer (TNBC) patients. TNBC is a heterogeneous malignancy with a varying prognosis. Recently, the importance of TILs in TNBC has been determined.
MATERIALS AND METHODS: We retrospectively enrolled 60 lesions of TNBC. Either at 1.5T or 3T MRI, including T1 , T2 -weighted, and dynamic contrast-enhanced, images were obtained. The CAD results for all lesions were obtained, and we analyzed quantitative kinetic features including the initial peak enhancement and enhancement profiles. We divided the tumors into two groups: those with a TIL level of less than 50%, and those with a TIL level of 50% or more. Kinetic parameters were compared using Student's t-tests and chi-square tests.
RESULTS: There were 48 low-TIL lesions and 12 high-TIL lesions. The portion of persistent enhancement of tumors was negatively associated with the TIL levels (P = 0.003). The persistent minus washout value of the low-TIL group was higher than that of the high-TIL group (P = 0.008). The odds ratios were 0.944 (P = 0.012) for the persistent portion and 0.971 (P = 0.008) for the persistent minus washout value.
CONCLUSION: The prediction model using kinetic enhancement parameters, particularly persistent proportion and plateau minus washout value, could be helpful for identifying TIL levels in TNBC and may be used as an imaging biomarker to guide the treatment plan. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:760-766.
© 2017 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  MRI; computer-aided detection; quantitative kinetic parameter; triple negative breast cancer; tumor-infiltrating lymphocytes

Mesh:

Year:  2017        PMID: 28675570     DOI: 10.1002/jmri.25802

Source DB:  PubMed          Journal:  J Magn Reson Imaging        ISSN: 1053-1807            Impact factor:   4.813


  9 in total

Review 1.  Potential Predictive and Prognostic Value of Biomarkers Related to Immune Checkpoint Inhibitor Therapy of Triple-Negative Breast Cancer.

Authors:  Qiaorui Tan; Sha Yin; Dongdong Zhou; Yajing Chi; Xiaochu Man; Huihui Li
Journal:  Front Oncol       Date:  2022-04-29       Impact factor: 5.738

2.  Usefulness of imaging findings in predicting tumor-infiltrating lymphocytes in patients with breast cancer.

Authors:  Filiz Çelebi; Filiz Agacayak; Alper Ozturk; Serkan Ilgun; Muhammed Ucuncu; Zeynep Erdogan Iyigun; Çetin Ordu; Kezban Nur Pilanci; Gul Alco; Serap Gultekin; Emetullah Cindil; Gursel Soybir; Fatma Aktepe; Vahit Özmen
Journal:  Eur Radiol       Date:  2019-12-10       Impact factor: 5.315

3.  Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging.

Authors:  Shuling Chen; Shiting Feng; Jingwei Wei; Fei Liu; Bin Li; Xin Li; Yang Hou; Dongsheng Gu; Mimi Tang; Han Xiao; Yingmei Jia; Sui Peng; Jie Tian; Ming Kuang
Journal:  Eur Radiol       Date:  2019-01-21       Impact factor: 5.315

4.  Kinetic volume analysis on dynamic contrast-enhanced MRI of triple-negative breast cancer: associations with survival outcomes.

Authors:  Yoko Hayashi; Hiroko Satake; Satoko Ishigaki; Rintaro Ito; Mariko Kawamura; Hisashi Kawai; Shingo Iwano; Shinji Naganawa
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

Review 5.  Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.

Authors:  Jia Wu; Aaron T Mayer; Ruijiang Li
Journal:  Semin Cancer Biol       Date:  2020-12-05       Impact factor: 17.012

6.  Correlation Between Mammographic Radiomics Features and the Level of Tumor-Infiltrating Lymphocytes in Patients With Triple-Negative Breast Cancer.

Authors:  Hongwei Yu; Xianqi Meng; Huang Chen; Xiaowei Han; Jingfan Fan; Wenwen Gao; Lei Du; Yue Chen; Yige Wang; Xiuxiu Liu; Lu Zhang; Guolin Ma; Jian Yang
Journal:  Front Oncol       Date:  2020-04-15       Impact factor: 6.244

7.  Whole-Lesion Histogram Analysis of the Apparent Diffusion Coefficient as a Quantitative Imaging Biomarker for Assessing the Level of Tumor-Infiltrating Lymphocytes: Value in Molecular Subtypes of Breast Cancer.

Authors:  Wen-Jie Tang; Zhe Jin; Yan-Ling Zhang; Yun-Shi Liang; Zi-Xuan Cheng; Lei-Xin Chen; Ying-Ying Liang; Xin-Hua Wei; Qing-Cong Kong; Yuan Guo; Xin-Qing Jiang
Journal:  Front Oncol       Date:  2021-01-08       Impact factor: 6.244

8.  Automatic identification of triple negative breast cancer in ultrasonography using a deep convolutional neural network.

Authors:  Heng Ye; Jing Hang; Meimei Zhang; Xiaowei Chen; Xinhua Ye; Jie Chen; Weixin Zhang; Di Xu; Dong Zhang
Journal:  Sci Rep       Date:  2021-10-14       Impact factor: 4.379

Review 9.  Radiomic biomarkers of tumor immune biology and immunotherapy response.

Authors:  Jarey H Wang; Kareem A Wahid; Lisanne V van Dijk; Keyvan Farahani; Reid F Thompson; Clifton David Fuller
Journal:  Clin Transl Radiat Oncol       Date:  2021-04-07
  9 in total

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