Correlation between 18F-FDG uptake on PET/MRI and the level of tumor-infiltrating lymphocytes (TILs) in triple-negative and HER2-positive breast cancer

Clinicopathological and molecular predictors of [18F]FDG-PET disease detection in HER2-positive early breast cancer: RESPONSE, a substudy of the randomized PHERGain trial

BACKGROUND

The PHERGain study (NCT03161353) is assessing early metabolic responses to neoadjuvant treatment with trastuzumab-pertuzumab and chemotherapy de-escalation using a [18Fluorine]fluorodeoxyglucose-positron emission tomography ([18F]FDG-PET) and a pathological complete response-adapted strategy in HER2-positive (HER2+) early breast cancer (EBC). Herein, we present RESPONSE, a PHERGain substudy, where clinicopathological and molecular predictors of [18F]FDG-PET disease detection were evaluated.

METHODS

A total of 500 patients with HER2 + EBC screened in the PHERGain trial with a tumor size > 1.5 cm by magnetic resonance imaging (MRI) were included in the RESPONSE substudy. PET[-] criteria entailed the absence of  ≥ 1 breast lesion with maximum standardized uptake value (SUVmax) ≥ 1.5 × SUVmean liver + 2 standard deviation. Among 75 PET[-] patients screened, 21 with SUVmax levels < 2.5 were randomly selected and matched with 21 PET[+] patients with SUVmax levels ≥ 2.5 based on patient characteristics associated with [18F]FDG-PET status. The association between baseline SUVmax and [18F]FDG-PET status ([-] or [+]) with clinicopathological characteristics was assessed. In addition, evaluation of stromal tumor-infiltrating lymphocytes (sTILs) and gene expression analysis using PAM50 and Vantage 3D™ Cancer Metabolism Panel were specifically compared in a matched cohort of excluded and enrolled patients based on the [18F]FDG-PET eligibility criteria.

RESULTS

Median SUVmax at baseline was 7.2 (range, 1-39.3). Among all analyzed patients, a higher SUVmax was associated with a higher tumor stage, larger tumor size, lymph node involvement, hormone receptor-negative status, higher HER2 protein expression, increased Ki67 proliferation index, and higher histological grade (p < 0.05). [18F]FDG-PET [-] criteria patients had smaller tumor size (p = 0.014) along with the absence of lymph node involvement and lower histological grade than [18F]FDG-PET [+] patients (p < 0.01). Although no difference in the levels of sTILs was found among 42 matched [18F]FDG-PET [-]/[+] criteria patients (p = 0.73), [18F]FDG-PET [-] criteria patients showed a decreased risk of recurrence (ROR) and a lower proportion of PAM50 HER2-enriched subtype than [18F]FDG-PET[+] patients (p < 0.05). Differences in the expression of genes involved in cancer metabolism were observed between [18F]FDG-PET [-] and [18F]FDG-PET[+] criteria patients.

CONCLUSIONS

These results highlight the clinical, biological, and metabolic heterogeneity of HER2+ breast cancer, which may facilitate the selection of HER2+ EBC patients likely to benefit from [18F]FDG-PET imaging as a tool to guide therapy.

TRIAL REGISTRATION

Clinicaltrials.gov; NCT03161353; registration date: May 15, 2017.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Does high [18F]FDG uptake always mean poor prognosis? Colon cancer with high-level microsatellite instability is associated with high [18F]FDG uptake on PET/CT

OBJECTIVES

High-level microsatellite instability (MSI-high) is generally associated with higher F-18 fluorodeoxyglucose ([18F]FDG) uptake than stable microsatellite (MSI-stable) tumors. However, MSI-high tumors have better prognosis, which is in contrast with general understanding that high [18F]FDG uptake correlates with poor prognosis. This study evaluated metastasis incidence with MSI status and [18F]FDG uptake.

METHODS

We retrospectively reviewed 108 right-side colon cancer patients who underwent preoperative [18F]FDG PET/CT and postoperative MSI evaluations using a standard polymerase chain reaction at five Bethesda guidelines panel loci. The maximum standard uptake value (SUVmax), SUVmax tumor-to-liver ratio (TLR), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) of the primary tumor were measured using SUV 2.5 cut-off threshold. Student's t-test or Mann-Whitney U test was performed for continuous variables, and χ2 test or Fisher's exact test was performed for categorical variables (p value of  < 0.05 for statistical significance). Medical records were reviewed for metastasis incidence.

RESULTS

Our study population had 66 MSI-stable and 42 MSI-high tumors. [18F]FDG uptake was higher in MSI-high tumors than MSI-stable tumors (TLR, median (Q1, Q3): 7.95 (6.06, 10.54) vs. 6.08 (4.09, 8.82), p = 0.021). Multivariable subgroup analysis demonstrated that higher [18F]FDG uptake was associated with higher risks of distant metastasis in MSI-stable tumors (SUVmax: p = 0.025, MTV: p = 0.008, TLG: p = 0.019) but not in MSI-high tumors.

CONCLUSION

MSI-high colon cancer is associated with high [18F]FDG uptake, but unlike MSI-stable tumors, the degree of [18F]FDG uptake does not correlate with the rate of distant metastasis.

CLINICAL RELEVANCE STATEMENT

MSI status should be considered during PET/CT assessment of colon cancer patients, as the degree of [18F]FDG uptake might not reflect metastatic potential in MSI-high tumors.

KEY POINTS

• High-level microsatellite instability (MSI-high) tumor is a prognostic factor for distant metastasis. • MSI-high colon cancers had a tendency of demonstrating higher [18F]FDG uptake compared to MSI-stable tumors. • Although higher [18F]FDG uptake is known to represent higher risks of distant metastasis, the degree of [18F]FDG uptake in MSI-high tumors did not correlate with the rate at which distant metastasis occurred.

The Clinical Added Value of Breast Cancer Imaging Using Hybrid PET/MR Imaging

Dedicated MR imaging is highly performant for the evaluation of the primary lesion and should regularly be added to whole-body PET/MR imaging for the initial staging. PET/MR imaging is highly sensitive for the detection of nodal involvement and could be combined with the high specificity of axillary second look ultrasound for the confirmation of the N staging. For M staging, with the exception of lung lesions, PET/MR imaging is superior to PET/computed tomography, at half the radiation dose. The predictive value of multiparametric imaging with PET/MR imaging holds promise to improve through radiomics and artificial intelligence.

[18F]FDG PET/CT for predicting triple-negative breast cancer outcomes after neoadjuvant chemotherapy with or without pembrolizumab

PURPOSE

To determine if pretreatment [18F]FDG PET/CT could contribute to predicting complete pathological complete response (pCR) in patients with early-stage triple-negative breast cancer (TNBC) undergoing neoadjuvant chemotherapy with or without pembrolizumab.

METHODS

In this retrospective bicentric study, we included TNBC patients who underwent [18F]FDG PET/CT before neoadjuvant chemotherapy (NAC) or chemo-immunotherapy (NACI) between March 2017 and August 2022. Clinical, biological, and pathological data were collected. Tumor SUVmax and total metabolic tumor volume (TMTV) were measured from the PET images. Cut-off values were determined using ROC curves and a multivariable model was developed using logistic regression to predict pCR.

RESULTS

N = 191 patients were included. pCR rates were 53 and 70% in patients treated with NAC (N = 91) and NACI (N = 100), respectively (p < 0.01). In univariable analysis, high Ki67, high tumor SUVmax (> 12.3), and low TMTV (≤ 3.0 cm3) were predictors of pCR in the NAC cohort while tumor staging classification (< T3), BRCA1/2 germline mutation, high tumor SUVmax (> 17.2), and low TMTV (≤ 7.3 cm3) correlated with pCR in the NACI cohort. In multivariable analysis, only high tumor SUVmax (NAC: OR 8.8, p < 0.01; NACI: OR 3.7, p = 0.02) and low TMTV (NAC: OR 6.6, p < 0.01; NACI: OR 3.5, p = 0.03) were independent factors for pCR in both cohorts, albeit at different thresholds.

CONCLUSION

High tumor metabolism (SUVmax) and low tumor burden (TMTV) could predict pCR after NAC regardless of the addition of pembrolizumab. Further studies are warranted to validate such findings and determine how these biomarkers could be used to guide neoadjuvant therapy in TNBC patients.

Evaluation of Breast Cancer Tumor-Infiltrating Lymphocytes on Ultrasound Images Based on a Novel Multi-Cascade Residual U-Shaped Network

OBJECTIVE

Breast cancer has become the leading cancer of the 21st century. Tumor-infiltrating lymphocytes (TILs) have emerged as effective biomarkers for predicting treatment response and prognosis in breast cancer. The work described here was aimed at designing a novel deep learning network to assess the levels of TILs in breast ultrasound images.

METHODS

We propose the Multi-Cascade Residual U-Shaped Network (MCRUNet), which incorporates a gray feature enhancement (GFE) module for image reconstruction and normalization to achieve data synergy. Additionally, multiple residual U-shaped (RSU) modules are cascaded as the backbone network to maximize the fusion of global and local features, with a focus on the tumor's location and surrounding regions. The development of MCRUNet is based on data from two hospitals and uses a publicly available ultrasound data set for transfer learning.

RESULTS

MCRUNet exhibits excellent performance in assessing TILs levels, achieving an area under the receiver operating characteristic curve of 0.8931, an accuracy of 85.71%, a sensitivity of 83.33%, a specificity of 88.64% and an F1 score of 86.54% in the test group. It outperforms six state-of-the-art networks in terms of performance.

CONCLUSION

The MCRUNet network based on breast ultrasound images of breast cancer patients holds promise for non-invasively predicting TILs levels and aiding personalized treatment decisions.

Tumor infiltrating lymphocytes and radiological picture of the tumor

Tumor microenvironment (TME) is a complex entity that includes besides the tumor cells also a whole range of immune cells. Among various populations of immune cells infiltrating the tumor, tumor infiltrating lymphocytes (TILs) are a population of lymphocytes characterized by high reactivity against the tumor component. As, TILs play a key role in mediating responses to several types of therapy and significantly improve patient outcomes in some cancer types including for instance breast cancer and lung cancer, their assessment has become a good predictive tool in the evaluation of potential treatment efficacy. Currently, the evaluation of the density of TILs infiltration is performed by histopathological. However, recent studies have shed light on potential utility of several imaging methods, including ultrasonography, magnetic resonance imaging (MRI), positron emission tomography-computed tomography (PET-CT), and radiomics, in the assessment of TILs levels. The greatest attention concerning the utility of radiology methods is directed to breast and lung cancers, nevertheless imaging methods of TILs are constantly being developed also for other malignancies. Here, we focus on reviewing the radiological methods used to assess the level of TILs in different cancer types and on the extraction of the most favorable radiological features assessed by each method.

Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment

With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.

Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer: The Role of Conventional Ultrasound and Contrast-Enhanced Ultrasound

OBJECTIVES

Tumor-infiltrating lymphocytes (TILs) have emerged as an efficient biomarker predicting treatment response and prognosis of breast cancer (BC). This study aimed to evaluate the association between conventional ultrasound and contrast-enhanced ultrasound (CEUS) imaging features with TIL levels in invasive BC patients.

METHODS

We retrospectively included 267 women with invasive BC who had undergone conventional ultrasound and CEUS. Patients were divided into low (≤10%) and high (>10%) TIL groups. Conventional ultrasound and CEUS features were analyzed by two sonographers. The associations between the TIL levels and imaging features were evaluated.

RESULTS

Of the 267 patients, 122 with high TILs and 145 with low TIL levels. High TIL tumors were more likely to have a circumscribed margin, oval or round shape, and enhanced posterior echoes on ultrasonography (p < 0.05). In contrast, low TIL tumors were more likely to have an irregular shape, un-circumscribed, indistinct and spiculated margin (p < 0.05). In CEUS, high TIL tumors showed a more regular shape, clearer margin, more homogeneous enhancement and higher peak intensity (PI) value (p < 0.05). Logistic analysis indicated that shape, posterior features, PI, and enhanced homogeneity were independent predictors for high TIL tumors. The model combined the four independent predictors have a moderate performance in predicting high TIL tumors with AUC 0.79, sensitivity 0.72, and specificity 0.78.

CONCLUSIONS

Conventional ultrasound and CEUS features were associated with TIL levels in invasive BC. Consequently, the results suggested that preoperative conventional ultrasound and CEUS may be a useful noninvasive imaging biomarker for individualized treatment decisions.

Deep Learning with Transformer or Convolutional Neural Network in the Assessment of Tumor-Infiltrating Lymphocytes (TILs) in Breast Cancer Based on US Images: A Dual-Center Retrospective Study

This study aimed to explore the feasibility of using a deep-learning (DL) approach to predict TIL levels in breast cancer (BC) from ultrasound (US) images. A total of 494 breast cancer patients with pathologically confirmed invasive BC from two hospitals were retrospectively enrolled. Of these, 396 patients from hospital 1 were divided into the training cohort (n = 298) and internal validation (IV) cohort (n = 98). Patients from hospital 2 (n = 98) were in the external validation (EV) cohort. TIL levels were confirmed by pathological results. Five different DL models were trained for predicting TIL levels in BC using US images from the training cohort and validated on the IV and EV cohorts. The overall best-performing DL model, the attention-based DenseNet121, achieved an AUC of 0.873, an accuracy of 79.5%, a sensitivity of 90.7%, a specificity of 65.9%, and an F1 score of 0.830 in the EV cohort. In addition, the stratified analysis showed that the DL models had good discrimination performance of TIL levels in each of the molecular subgroups. The DL models based on US images of BC patients hold promise for non-invasively predicting TIL levels and helping with individualized treatment decision-making.

Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment

Breast cancer is the most common malignancy in women, and some subtypes are associated with a poor prognosis with a lack of efficacious therapy. Moreover, immunotherapy and the use of other novel antibody‒drug conjugates have been rapidly incorporated into the standard management of advanced breast cancer. To extract more benefit from these therapies, clarifying and monitoring the tumor microenvironment (TME) status is critical, but this is difficult to accomplish based on conventional approaches. Radiomics is a method wherein radiological image features are comprehensively collected and assessed to build connections with disease diagnosis, prognosis, therapy efficacy, the TME, etc In recent years, studies focused on predicting the TME using radiomics have increasingly emerged, most of which demonstrate meaningful results and show better capability than conventional methods in some aspects. Beyond predicting tumor-infiltrating lymphocytes, immunophenotypes, cytokines, infiltrating inflammatory factors, and other stromal components, radiomic models have the potential to provide a completely new approach to deciphering the TME and facilitating tumor management by physicians.

Relationship between FDG-PET and the immune microenvironment in breast cancer

OBJECTIVE

To investigate the relationship between fluorodeoxyglucose (FDG) uptake (maximum standardised uptake value [SUVmax]) and immune markers (tumour-infiltrating lymphocytes [TILs] and neutrophil-to-lymphocyte ratio [NLR]) and evaluate the potential prognostic value of any correlations.

METHODS

Data from 502 patients with breast cancer, including 346 oestrogen receptor (ER)-positive / human epidermal growth factor receptor 2 (HER2)-negative, 88 HER2-positive, and 68 triple-negative cases, who had undergone surgery were reviewed. Relationships between the clinicopathological factors, SUVmax, TILs, NLR, recurrence-free survival (RFS), and overall survival of all patients and each subtype were evaluated using a Cox proportional hazards model and log-rank test. A sub-analysis of patients divided into low and high TIL groups was also undertaken.

RESULTS

High SUVmax was significantly related to high TILs (p < 0.0001). In low TIL (TILs1) group, patients with high SUVmax (≥3.585) had a significantly shorter RFS than those with low SUVmax (<3.585; p < 0.0001). In high TIL (TILs2,3) group, patients with high SUVmax had a shorter RFS than those with low SUVmax without a significant difference (p = 0.35). Multivariate analysis of 502 patients showed high SUVmax, high T status, and nodal metastasis were independent negative predictors of RFS. In 317 TILs-low patients, high SUVmax, high T status, nodal metastasis, and ER-positivity were independent predictors of RFS. In 185 TILs-high patients, nodal metastasis was an independent predictor of RFS. In ER-positive/HER2-negative and HER2-positive subtypes, SUVmax was a significant predictive parameter in the TILs-low but not TILs-high groups.

CONCLUSION

FDG uptake may be predictive of immunological features and aggressive features in breast cancer patients.

Qin Huang formula enhances the effect of Adriamycin in B-cell lymphoma via increasing tumor infiltrating lymphocytes by targeting toll-like receptor signaling pathway

Background

As an original traditional Chinese medicinal formula, Qin Huang formula (QHF) is used as adjuvant therapy for treating lymphoma in our hospital and has proven efficacy when combined with chemotherapy. However, the underlying mechanisms of QHF have not been elucidated.

Methods

A network pharmacological-based analysis method was used to screen the active components and predict the potential mechanisms of QHF in treating B cell lymphoma. Then, a murine model was built to verify the antitumor effect of QHF combined with Adriamycin (ADM) in vivo. Finally, IHC, ELISA, ¹⁸F-FDG PET-CT scan, and western blot were processed to reveal the intriguing mechanism of QHF in treating B cell lymphoma.

Results

The systemic pharmacological study revealed that QHF took effect following a multiple-target and multiple-pathway pattern in the human body. In vivo study showed that combination therapy with QHF and ADM potently inhibited the growth of B cell lymphoma in a syngeneic murine model, and significantly increased the proportion of tumor infiltrating CD4+ and CD8+ T cells in the tumor microenvironment (TME). Furthermore, the level of CXCL10 and IL-6 was significantly increased in the combination group. Finally, the western blot exhibited that the level of TLR2 and p38 MAPK increased in the combination therapy group.

Conclusion

QHF in combination of ADM enhances the antitumor effect of ADM via modulating tumor immune microenvironment and can be a combination therapeutic strategy for B cell lymphoma patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03660-8.

Increased tumor glycolysis is associated with decreased immune infiltration across human solid tumors

Response to immunotherapy across multiple cancer types is approximately 25%, with some tumor types showing increased response rates compared to others (i.e. response rates in melanoma and non-small cell lung cancer (NSCLC) are typically 30-60%). Patients whose tumors are resistant to immunotherapy often lack high levels of pre-existing inflammation in the tumor microenvironment. Increased tumor glycolysis, acting through glucose deprivation and lactic acid accumulation, has been shown to have pleiotropic immune suppressive effects using in-vitro and in-vivo models of disease. To determine whether the immune suppressive effect of tumor glycolysis is observed across human solid tumors, we analyzed glycolytic and immune gene expression patterns in multiple solid malignancies. We found that increased expression of a glycolytic signature was associated with decreased immune infiltration and a more aggressive disease across multiple tumor types. Radiologic and pathologic analysis of untreated estrogen receptor (ER)-negative breast cancers corroborated these observations, and demonstrated that protein expression of glycolytic enzymes correlates positively with glucose uptake and negatively with infiltration of CD3⁺ and CD8⁺ lymphocytes. This study reveals an inverse relationship between tumor glycolysis and immune infiltration in a large cohort of multiple solid tumor types.

Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma

Background

High levels of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of ¹⁸F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.

Results

Using an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In ¹⁸F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan–Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower ¹⁸F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P = .001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P = .027). A high TLR (P = .01) score of ¹⁸F-FDG PET parameters and a high T follicular helper cell (TFH) score (P = .005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P = .024 and .047).

Conclusions

A high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00908-9.

Stromal tumor-infiltrating lymphocytes level as a prognostic factor for resected intrahepatic cholangiocarcinoma and its prediction by apparent diffusion coefficient

BACKGROUND

Tumor-infiltrating lymphocytes (TILs) are a prognostic factor or an indicator of chemotherapy response for various malignancies. The aim of this study was to investigate the prognostic impact of TILs in resected intrahepatic cholangiocarcinoma (IHCC). We also investigated the usefulness of the apparent diffusion coefficient (ADC) in diffusion-weighted magnetic resonance imaging (DW-MRI) to predict TILs.

METHODS

We enrolled 23 patients with IHCC who underwent initial hepatic resection in Tokushima University Hospital from 2006 to 2017. We evaluated stromal TILs in the tumor marginal area and central area in surgical specimens. Patients were divided into low vs high stromal TILs groups. We analyzed the patients' clinicopathological factors, including prognosis, according to the degree of stromal TILs. We also analyzed the correlation between stromal TILs and the minimum ADC value.

RESULTS

Stromal TILs in the marginal area reflected overall survival more accurately than that in the central area. Additionally, marginal low TILs was significantly associated with lymph node metastasis and portal vein invasion. Both overall- and disease-free survival rates in the marginal low TILs group were significantly worse than those in the marginal high TILs group (P < 0.05). In the multivariate analysis, marginal low TILs were an independent prognostic factor for both overall- and disease-free survival (P < 0.05), and marginal low TILs were significantly associated with lower minimum ADC values (P < 0.02).

CONCLUSIONS

Stromal TILs, especially in the marginal area, might demonstrate prognostic impact in patients with IHCC. Moreover, the ADC values from MRI may predict TILs in IHCC tumor tissue.

Evaluating Tumor-Infiltrating Lymphocytes in Breast Cancer Using Preoperative MRI-Based Radiomics

BACKGROUND

Evaluating tumor-infiltrating lymphocytes (TILs) in patients with breast cancer using radiomics has been rarely explored.

PURPOSE

To establish a radiomics nomogram based on dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for preoperatively evaluating TIL level.

STUDY TYPE

Retrospective.

POPULATION

A total of 154 patients with breast cancer were divided into a training cohort (N = 87) and a test cohort (N = 67), who were further divided into low TIL (<50%) and high TIL (≥50%) subgroups according to the histopathological results.

FIELD STRENGTH/SEQUENCE

3.0 T; axial T2-weighted imaging (fast spin echo), diffusion-weighted imaging (spin echo-echo planar imaging), and the volume imaging for breast assessment DCE sequence (gradient recalled echo).

ASSESSMENT

A radiomics signature was developed from the training dataset and independent risk factors were selected by multivariate logistic regression to build a clinical model. A nomogram model was built by combining radiomics score and risk factors. The performance of the nomogram was assessed using calibration curves and decision curves. The area under the receiver operating characteristic (ROC) curve, accuracy, sensitivity, and specificity were calculated.

STATISTICAL TESTS

The least absolute shrinkage and selection operator, univariate and multivariate logistic regression analysis, t-tests and chi-squared tests or Fisher's exact test, Hosmer-Lemeshow test, ROC analysis, and decision curve analysis were conducted. P < 0.05 was considered statistically significant.

RESULTS

The radiomics signature and nomogram model exhibited better calibration and validation performance in the training (radiomics: area under the curve [AUC] 0.86; nomogram: AUC 0.88) and test (radiomics: AUC 0.83; nomogram: AUC 0.84) datasets compared with clinical model (training: AUC 0.76; test: AUC 0.72). The decision curve demonstrated that the nomogram model exhibited better performance than the clinical model, with a threshold probability between 0.15 and 0.9.

DATA CONCLUSION

The nomogram model based on preoperative MRI exhibited an excellent ability for the noninvasive evaluation of TILs in breast cancer.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY STAGE: 2.

Human Epidermal Growth Factor Receptor Type 2-Positive Breast Cancer: Association of MRI and Clinicopathologic Features With Tumor-Infiltrating Lymphocytes

Background: Tumor-infiltrating lymphocytes (TILs) are associated with therapeutic outcomes and prognosis in patients with human epidermal growth factor receptor type 2-positive (HER2+) breast cancer. Identification of TIL levels is clinically relevant. Objective: To explore associations of clinicopathologic and MRI features with TIL levels in patients with HER2+ breast cancer. Methods: A total of 212 consecutive women (mean age, 54 years) diagnosed with HER2+ breast cancer between January 2017 and December 2019 were included in this retrospective study. Patients were divided into low (<10%) and high (≥10%) TIL groups. Three breast radiologists independently reviewed images; interreader agreement was assessed, and the first readers' findings were used for further analysis. Associations of clinicopathologic and MRI features with TIL levels were evaluated using multivariable logistic regression analysis. Subanalysis of TIL levels by hormone receptor (HR) status was also performed. Results: A total of 115 (54.2%) patients had low, and 97 (45.8%) had high, TIL levels. High TIL level was associated (all p<.05) with histologic grade 3 (odds ratio [OR]=3.98; frequency of 78.4% vs 52.2% in high vs low TIL groups, respectively), high tumor cellularity (OR=4.59; median cellularity of 60% vs 50%), lower frequency of associated ductal carcinoma in situ (OR=0.16; frequency of 86.6% vs 94.8%), and higher frequency of peritumoral edema on T2-weighted images (OR=2.83; 71.1% vs 50.4%). In subgroup analysis by HR status, histologic grade 3 (OR=5.03, p=.002) was a significant independent predictor of high TIL in the HR+/HER2+ group, while high tumor cellularity (OR=9.06, p=.002), peritumoral edema (OR=5.23, p=.03), and low ADC (OR=11.69, p=.047) were independent predictors of high TIL in the HR-/HER2+ group. Interreader agreement for peritumoral edema was moderate among the three radiologists (к, range 0.432-0.539). Conclusion: Peritumoral edema on MRI and histopathologic feature of tumor aggressiveness help predict high TIL levels in patients with HER2+ breast cancer. Clinical Impact: Pretreatment MRI features may serve as a useful tool for assessing TIL levels in patients with HER2+ breast cancer, helping to classify patients with variable clinical outcomes related to immune activity and to guide selection among neoadjuvant chemotherapy (NAC) or HER2-targeted therapy or immunotherapy.

Performance of radiomics models for tumour-infiltrating lymphocyte (TIL) prediction in breast cancer: the role of the dynamic contrast-enhanced (DCE) MRI phase

OBJECTIVE

To systematically investigate the effect of imaging features at different DCE-MRI phases to optimise a radiomics model based on DCE-MRI for the prediction of tumour-infiltrating lymphocyte (TIL) levels in breast cancer.

MATERIALS AND METHODS

This study retrospectively collected 133 patients with pathologically proven breast cancer, including 73 patients with low TIL levels and 60 patients with high TIL levels. The volumes of breast cancer lesions were manually delineated on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and each phase of DCE-MRI, followed by 6250 quantitative feature extractions. The least absolute shrinkage and selection operator (LASSO) method was used to select predictive feature sets for the classifiers. Four models were developed for predicting TILs: (1) single enhanced phase radiomics models; (2) fusion enhanced multi-phase radiomics models; (3) fusion multi-sequence radiomics models; and (4) a combined radiomics-based clinical model.

RESULTS

Image features extracted from the delayed phase MRI, especially DCE_Phase 6 (DCE_P6), demonstrated dominant predictive performances over features from other phases. The fusion multi-sequence radiomics model and combined radiomics-based clinical model achieved the highest predictive performances with areas under the curve (AUCs) of 0.934 and 0.950, respectively; however, the differences were not statistically significant.

CONCLUSION

The DCE-MRI radiomics model, especially image features extracted from the delayed phases, can help improve the performance in predicting TILs. The radiomics nomogram is effective in predicting TILs in breast cancer.

KEY POINTS

• Radiomics features extracted from DCE-MRI, especially delayed phase images, help predict TIL levels in breast cancer. • We developed a nomogram based on MRI to predict TILs in breast cancer that achieved the highest AUC of 0.950.

PET/MRI for Staging the Axilla in Breast Cancer: Current Evidence and the Rationale for SNB vs. PET/MRI Trials

Simple Summary

PET/MRI is a relatively new, hybrid imaging tool that allows practitioners to obtain both a local and systemic staging in breast cancer patients in a single exam. To date, the available evidence is not sufficient to determine the role of PET/MRI in breast cancer management. The aims of this paper are to provide an overview of the current literature on PET/MRI in breast cancer, and to illustrate two ongoing trials aimed at defining the eventual role of PET/MRI in axillary staging in two different settings: patients with early breast cancer and patients with positive axillary nodes that are candidates for primary systemic therapy. In both cases, findings from PET/MRI will be compared with the final pathology and could be helpful to better tailor axillary surgery in the future.

Abstract

Axillary surgery in breast cancer (BC) is no longer a therapeutic procedure but has become a purely staging procedure. The progressive improvement in imaging techniques has paved the way to the hypothesis that prognostic information on nodal status deriving from surgery could be obtained with an accurate diagnostic exam. Positron emission tomography/magnetic resonance imaging (PET/MRI) is a relatively new imaging tool and its role in breast cancer patients is still under investigation. We reviewed the available literature on PET/MRI in BC patients. This overview showed that PET/MRI yields a high diagnostic performance for the primary tumor and distant lesions of liver, brain and bone. In particular, the results of PET/MRI in staging the axilla are promising. This provided the rationale for two prospective comparative trials between axillary surgery and PET/MRI that could lead to a further de-escalation of surgical treatment of BC. • SNB vs. PET/MRI 1 trial compares PET/MRI and axillary surgery in staging the axilla of BC patients undergoing primary systemic therapy (PST). • SNB vs. PET/MRI 2 trial compares PET/MRI and sentinel node biopsy (SNB) in staging the axilla of early BC patients who are candidates for upfront surgery. Finally, these ongoing studies will help clarify the role of PET/MRI in BC and establish whether it represents a useful diagnostic tool that could guide, or ideally replace, axillary surgery in the future.

Correlation Between 18F-FDG Uptake and Immune Cell Infiltration in Metastatic Brain Lesions

Background

The purpose of this study was to investigate the correlation between ¹⁸F-fluorodeoxyglucose (FDG) uptake and infiltrating immune cells in metastatic brain lesions.

Methods

This retrospective study included 34 patients with metastatic brain lesions who underwent brain ¹⁸F-FDG positron emission tomography (PET)/computed tomography (CT) followed by surgery. ¹⁸F-FDG uptake ratio was calculated by dividing the standardized uptake value (SUV) of the metastatic brain lesion by the contralateral normal white matter uptake value. We investigated the clinicopathological characteristics of the patients and analyzed the correlation between ¹⁸F-FDG uptake and infiltration of various immune cells. In addition, we evaluated immune-expression levels of glucose transporter 1 (GLUT1), hexokinase 2 (HK2), and Ki-67 in metastatic brain lesions.

Results

The degree of ¹⁸F-FDG uptake of metastatic brain lesions was not significantly correlated with clinical parameters. There was no significant relationship between the ¹⁸F-FDG uptake and degree of immune cell infiltration in brain metastasis. Furthermore, other markers, such as GLUT1, HK2, and Ki-67, were not correlated with degree of ¹⁸F-FDG uptake. In metastatic brain lesions that originated from breast cancer, a higher degree of ¹⁸F-FDG uptake was observed in those with high expression of CD68.

Conclusions

In metastatic brain lesions, the degree of ¹⁸F-FDG uptake was not significantly associated with infiltration of immune cells. The ¹⁸F-FDG uptake of metastatic brain lesions from breast cancer, however, might be associated with macrophage activity.

Relationship of the standard uptake value of 18F-FDG-PET-CT with tumor-infiltrating lymphocytes in breast tumors measuring ≥ 1 cm

Evidence suggests that tumor cells and tumor-infiltrating lymphocytes (TILs) compete for glucose in the tumor microenvironment and that tumor metabolic parameters correlate with localized immune markers in several solid tumors. We investigated the relationship of the standardized uptake value (SUV) of ¹⁸F-fluorodeoxyglucose positron emission tomography computed tomography (¹⁸F-FDG-PET-CT) with stromal TIL levels in breast cancer. We included 202 patients who underwent preoperative ¹⁸F-FDG-PET-CT and had a tumor measuring ≥ 1 cm. Maximum SUV (SUVmax) was determined using ¹⁸F-FDG-PET-CT. Multiple logistic regression was used to identify factors related to high TIL levels (≥ 40%). All tumors were treatment naïve. A significant and weak correlation existed between continuous SUVmax and continuous TIL levels (p = 0.002, R = 0.215). Tumors with high SUVmax (≥ 4) had higher mean TIL levels than those with low SUVmax (< 4). In multivariable analysis, continuous SUVmax was an independent factor associated with high TIL levels; each 1-unit increment in SUVmax corresponded to an odds ratio of 1.14 (95% confidence interval: 1.01–1.29) for high TIL levels. Our study implies that SUV is associated with TILs in breast cancer and provides clinical evidence that elevated glucose uptake by breast tumors can predict the immune system-activated tumor micromilieu.

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

Purpose

To 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.

Methods

In 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 (10^th, 25^th, 50^th, 75^th, and 90^th 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.

Results

A statistically significant difference existed in the 10^th and 25^th 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 10^th 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).

Conclusions

Our findings suggest that whole-lesion ADC histogram parameters can be used as surrogate biomarkers to evaluate TIL levels in molecular subtypes of breast cancer.