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

Çelebi, Filiz; Agacayak, Filiz; Ozturk, Alper; Ilgun, Serkan; Üçüncü, Muhammed Zübeyr; İyigün, Zeynep Erdoğan; Ordu, Çetın; Pilancı, Kezban Nur; Alço, Gül; Gültekin, Serap; Cındıl, Emetullah; Soybir, Gürsel; Aktepe, Fatma; Özmen, Vahit

doi:10.1007/s00330-019-06516-x

Cited by 26 publications

(33 citation statements)

References 21 publications

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“…In this study, we found a significant difference in the 10 th and 25 th percentile ADC values between the low and high TIL levels. In the reports of Celebi et al ( 12 ) and Fogante et al ( 16 ), ROIs were drawn in the solid component of the tumor avoiding necrotic, cystic or hemorrhagic areas, that is, the region of the minimum ADC. They reported that the low TIL group showed significantly lower ADC values than the high TIL group.…”

Section: Discussionmentioning

confidence: 99%

“…They offer the advantage of serial evaluations and longitudinal measurements (before and after treatment) and enable spatial and temporal heterogeneity visualization ( 11 ). Recently, an increasing number of studies have focused on ultrasonography (US) ( 12 ), MRI ( 12 – 14 ), and PET ( 15 ) to assessed the correlation of imaging features and TIL levels. However, many imaging features in these studies are based on subjective judgments and lack objective quantitative indicators.…”

Section: Introductionmentioning

confidence: 99%

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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

et al. 2021

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PurposeTo assess whether apparent diffusion coefficient (ADC) metrics can be used to assess tumor-infiltrating lymphocyte (TIL) levels in breast cancer, particularly in the molecular subtypes of breast cancer.MethodsIn total, 114 patients with breast cancer met the inclusion criteria (mean age: 52 years; range: 29–85 years) and underwent multi-parametric breast magnetic resonance imaging (MRI). The patients were imaged by diffusion-weighted (DW)-MRI (1.5 T) using a single-shot spin-echo echo-planar imaging sequence. Two readers independently drew a region of interest (ROI) on the ADC maps of the whole tumor. The mean ADC and histogram parameters (10th, 25th, 50th, 75th, and 90th percentiles of ADC, skewness, entropy, and kurtosis) were used as features to analyze associations with the TIL levels in breast cancer. Additionally, the correlation between the ADC values and Ki-67 expression were analyzed. Continuous variables were compared with Student’s t-test or Mann-Whitney U test if the variables were not normally distributed. Categorical variables were compared using Pearson’s chi-square test or Fisher’s exact test. Associations between TIL levels and imaging features were evaluated by the Mann-Whitney U and Kruskal-Wallis tests.ResultsA statistically significant difference existed in the 10th and 25th percentile ADC values between the low and high TIL groups in breast cancer (P=0.012 and 0.027). For the luminal subtype of breast cancer, the 10th percentile ADC value was significantly lower in the low TIL group (P=0.041); for the non-luminal subtype of breast cancer, the kurtosis was significantly lower in the low TIL group (P=0.023). The Ki-67 index showed statistical significance for evaluating the TIL levels in breast cancer (P=0.007). Additionally, the skewness was significantly higher for samples with high Ki-67 levels in breast cancer (P=0.029).ConclusionsOur findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Over the past few years, tumor microenvironment has been one of the fastest developing and most promising fields in breast cancer research. An increasing number of studies have investigated that immune microenvironment not only modulates immunotherapy but also promotes prognosis of patients with breast cancer (1)(2)(3). To estimate the stromal and immune infiltration level of tumor and provide clues for researches on this field, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) (4) is modeled and tested to calculate tumor purity, stromal and immune scores of patients via expression profile and give an overall view of tumor microenvironment.…”

Section: Introductionmentioning

confidence: 99%

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

et al. 2020

Front. Med.

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Background: Tumor microenvironment is essential for breast cancer progression and metastasis. Our study sets out to examine the genes affecting stromal and immune infiltration in breast cancer progression and prognosis. Materials and Methods: This work provides an approach for quantifying stromal and immune scores by using ESTIMATE algorithm based on gene expression matrix of breast cancer patients in TCGA database. We found differentially expressed genes (DEGs) through limma R package. Functional enrichments were accessed through Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Besides, we constructed a protein-protein network, identified several hub genes in Cytoscape, and discovered functionally similar genes in GeneMANIA. Hub genes were validated with prognostic data by Kaplan-Meier analysis both in The Cancer Genome Atlas (TCGA) database and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) database and a meta-analysis of hub genes prognosis data was utilized in multiple databases. Furthermore, their relationship with infiltrating immune cells was evaluated by Tumor IMmune Estimation Resource (TIMER) web tool. Cox regression was utilized for overall survival (OS) and recurrence-free survival (RFS) in TCGA database and OS in METABRIC database in order to evaluate the impact of stromal and immune scores on patients prognosis. Results: One thousand and eighty-five breast cancer patients were investigated and 480 differentiated expressed genes (DEGs) were found based on the analysis of mRNA expression profiles. Functional analysis of DEGs revealed their potential functions in immune response and extracellular interaction. Protein-protein interaction network gave evidence of 10 hub genes. Some of the hub genes could be used as predictive markers for patients prognosis. In this study, we found that tumor purity and specific immune cells infiltration varied in response to hub genes expression. The multivariate cox regression highlighted the fact that immune score played a detrimental role in overall survival (HR = 0.45, 95% CI: 0.27-0.74, p = 0.002) and recurrence-free survival (HR = 0.41, 95% CI: 0.22-0.77, p = 0.006) in TCGA database. These result was confirmed in Xu et al. Tumor Microenvironment in Breast Cancer METABRIC database that immune score was a protector of OS (HR = 0.88, 95% CI: 0.77-0.99, p = 0.039). Conclusions: Our findings promote a better understanding of the potential genes behind the regulation of tumor microenvironment and cells infiltration. Immune score should be considered as a prognostic factor for patients' survival.

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“…Magnetic resonance imaging (MRI) plays an important role in the diagnosis of breast cancer, and a few recent studies reported its correlation with the clinical decision among breast cancer patients ( 14 , 15 ). In addition, several studies have shown that quantitative imaging features from MRI can predict TIL levels and molecular subtypes in patients with breast cancer ( 16 , 17 ). On the other hand, mammography is a simple, convenient, and low-cost examination without contrast agent injection, compared with MRI.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features

Meng

Huang

et al. 2021

Front. Oncol.

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ObjectivesThis study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer.MethodsData 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.ResultsAmong 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).ConclusionRadiomics 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.

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

Cited by 26 publications

References 21 publications

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

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

Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features

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