Tumor-stromal ratio (TSR) of invasive breast cancer: correlation with multi-parametric breast MRI findings

Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma

Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (P < .001 to P = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], P < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], P < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. Keywords: MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 Supplemental material is available for this article. © RSNA, 2024.

Magnetic resonance imaging features of invasive breast cancer association with the tumour stromal ratio

OBJECTIVE

To assess the association between breast cancer tumour stroma and magnetic resonance imaging (MRI) features.

MATERIALS AND METHODS

A total of 84 patients with treatment-naïve invasive breast cancer were enrolled into this retrospective study. The tumour stroma ratio (TSR) was estimated from the amount of tumour stroma in the pathology specimen of the breast tumour. The MRI images of the patients were analysed based on Breast Imaging Reporting and Data Systems (ACR-BIRADS) for qualitative features which include T2- weighted, diffusion-weighted images (DWI) and dynamic contrast-enhanced (DCE) for kinetic features. The mean signal intensity (SI) of Short Tau Inversion Recovery (STIR), with the ratio of STIR of the lesion and pectoralis muscle (L/M ratio) and apparent diffusion coefficient (ADC) value, were measured for the quantitative features. Correlation tests were performed to assess the relationship between TSR and MRI features.

RESULTS

There was a significant correlation between the margin of mass, enhancement pattern, and STIR signal intensity of breast cancer and TSR. There were 54.76% (n = 46) in the low stromal group and 45.24% (n = 38) in the high stromal group. A significant association were seen between the margin of the mass and TSR (p = 0.034) between the L/M ratio (p <0.001), and between STIR SI of the lesion and TSR (p<0.001). The median L/M ratio was significantly higher in the high TSR group as compared to the lower TSR group (p < 0.001).

CONCLUSION

Breast cancer with high stroma had spiculated margins, lower STIR signal intensity, and a heterogeneous pattern of enhancement. Hence, in this preliminary study, certain MRI features showed a potential to predict TSR.

Reassessing Breast Cancer-Associated Fibroblasts (CAFs) Interactions with Other Stromal Components and Clinico-Pathologic Parameters by Using Immunohistochemistry and Digital Image Analysis (DIA)

BACKGROUND

Breast cancer (BC) stroma has CD34- and αSMA-positive cancer-associated fibroblasts (CAFs) differently distributed. During malignant transformation, CD34-positive fibroblasts decrease while αSMA-positive CAFs increase. The prevalence of αSMA-positive CAFs in BC stroma makes microscopic examination difficult without digital image analysis processing (DIA). DIA was used to compare CD34- and αSMA-positive CAFs among breast cancer molecular subgroups. DIA-derived data were linked to age, survival, tumor stroma vessels, tertiary lymphoid structures (TLS), invasion, and recurrence.

METHODS

Double immunostaining for CD34 and αSMA showed different CAF distribution patterns in normal and BC tissues. Single CD34 immunohistochemistry on supplemental slides quantified tumor stroma CD34_CAFs. Digital image analysis (DIA) data on CAF density, intensity, stromal score, and H-score were correlated with clinico-pathologic factors.

RESULTS

CD34/αSMA CAF proportion was significantly related to age in Luminal A (LA), Luminal B (LB), and HER2 subtypes. CD34_CAF influence on survival, invasion, and recurrence of LA, LB-HER2, and TNBC subtypes was found to be significant. The CD34/αSMA-expressing CAFs exhibited a heterogeneous impact on stromal vasculature and TLS.

CONCLUSION

BC stromal CD34_CAFs/αSMA_CAFs have an impact on survival, invasion, and recurrence differently between BC molecular subtypes. The tumor stroma DIA assessment may have predictive potential to prognosis and long-term follow-up of patients with breast cancer.

The use of quantitative T1-mapping to identify cells and collagen fibers in rectal cancer

Aim

This study aimed to explore the value of T1 mapping in assessing the grade and stage of rectal adenocarcinoma and its correlation with tumor tissue composition.

Methods

Informed consent was obtained from all rectal cancer patients after approval by the institutional review board. Twenty-four patients (14 women and 10 men; mean age, 64.46 years; range, 35 - 82 years) were enrolled in this prospective study. MRI examinations were performed using 3.0T MR scanner before surgery. HE, immunohistochemical, and masson trichrome-staining was performed on the surgically resected tumors to assess the degree of differentiation, stage, and invasion. Two radiologists independently analyzed native T1 and postcontrast T1 for each lesion, and calculated the extracellular volume (ECV) was calculated from T1 values. Intraclass correlation coefficient (ICC) and Bland-Altman plots were applied to analyze the interobserver agreement of native T1 values and postcontrast T1 values. Student's t-test and one-way analysis of variance (ANOVA) were used to test the differences between T1 mapping parameters and differentiation types, T and N stages, and venous and neural invasion. Pearson correlation coefficients were used to analyze the correlation of T1 mapping extraction parameters with caudal type homeobox 2 (CDX-2), Ki-67 index, and collagen expression.

Results

Both the native and postcontrast T1 values had an excellent interobserver agreement (ICC 0.945 and 0.942, respectively). Postcontrast T1 values indicated significant differences in venous invasion (t=2.497, p=0.021) and neural invasion (t=2.254, p=0.034). Pearson's correlation analysis showed a significant positive correlation between native T1 values and Ki-67 (r=-0.407, p=0.049). There was a significant positive correlation between ECV and collagen expression (r=0.811, p=.000) and a significant negative correlation between ECV and CDX-2 (r=-0.465, p=0.022) and Ki-67 (r=-0.549, p=0.005).

Conclusion

Postcontrast T1 value can be used to assess venous and neural invasion in rectal cancer. ECV measurements based on T1 mapping can be used to identify cells and collagen fibers in rectal cancer.

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.

Investigating the role of core needle biopsy in evaluating tumor-stroma ratio (TSR) of invasive breast cancer: a retrospective study

PURPOSE

Tumor-stroma ratio (TSR) of invasive breast carcinoma has gained attention in recent years due to its prognostic significance. Previous studies showed TSR is a potential biomarker for indicating the tumor response to neoadjuvant chemotherapy. However, it is not clear how well TSR evaluation in biopsy specimens might reflect the TSR in resection specimens. We conducted a study to investigate whether biopsy evaluation of TSR can be an alternative method.

METHOD

We collected cases with invasive breast carcinoma of no special type (IBC-NST) from University of Yamanashi hospital between 2011 and 2017 whose biopsy and resection specimens both had a pathologically diagnosis of IBC-NST (n = 146). We conceptualized a method for evaluating TSR in biopsy specimens within a preliminary cohort (n = 50). Within the studied cohort (n = 96), biopsy-based TSR (b-TSR) and resection-based TSR (r-TSR) were scored by two pathologists. We then evaluated our method's validity and performance by measuring interobserver variability between the two pathologists, Spearman's correlation between b-TSR and r-TSR, and the receiver operating characteristics (ROC) analysis for defining stroma-rich and stroma-poor tumors.

RESULTS

Intra-class coefficient between the two pathologists was 0.59. The correlation coefficients between b-TSR and r-TSR in the two pathologists were 0.45 and 0.37. The ROC areas under the curve were 0.7 and 0.67. By considering an r-TSR of < 50% as stroma-rich, the sensitivity and specificity of detecting stroma-rich tumors were 64.1% and 66.7%, respectively, when b-TSR was < 40%.

CONCLUSION

Our current b-TSR evaluation method can provide information about r-TSR and facilitate pre-treatment therapy follow-up.

Diagnostic performance of DCE-MRI, multiparametric MRI and multimodality imaging for discrimination of breast non-mass-like enhancement lesions

OBJECTIVE

The aim of this study was to investigate and compare the diagnostic performance of dynamic contrast-enhanced (DCE)-MRI, multiparametric MRI (mpMRI), and multimodality imaging (MMI) combining mpMRI and mammography (MG) for discriminating breast non-mass-like enhancement (NME) lesions.

METHODS

This retrospective study enrolled 193 patients with 199 lesions who underwent 3.0 T MRI and MG from January 2017 to December 2019. The features of DCE-MRI, turbo inversion recovery magnitude (TIRM), and diffusion-weighted imaging (DWI) were assessed by two breast radiologists. Then, all lesions were divided into microcalcification and non-microcalcification groups to assess the features of MG. Comparisons were performed between groups using univariate analyses. Then, multivariate analyses were performed to construct diagnostic models for distinguishing NME lesions. Diagnostic performance was evaluated by using the area under the curve (AUC) and the differences between AUCs were evaluated by using the DeLong test.

RESULTS

Overall (n = 199), mpMRI outperformed DCE-MRI alone (AUCmpMRI = 0.924 vs. AUCDCE-MRI = 0.884; p = 0.007). Furthermore, MMI outperformed both mpMRI and MG (the microcalcification group [n = 140]: AUCMMI = 0.997 vs. AUCmpMRI = 0.978, p = 0.018 and AUCMMI = 0.997 vs. AUCMG = 0.912, p < 0.001; the non-microcalcification group [n = 59]: AUCMMI = 0.857 vs. AUCmpMRI = 0.768, p = 0.044 and AUCMMI = 0.857 vs. AUCMG = 0.759, p = 0.039).

CONCLUSION & ADVANCES IN KNOWLEDGE

DCE-MRI combined with DWI and TIRM information could improve the diagnostic performance for discriminating NME lesions compared with DCE-MRI alone. Furthermore, MMI combining mpMRI and MG showed better discrimination than both mpMRI and MG.

Rapid multi-dynamic algorithm for gray image analysis of the stroma percentage on colorectal cancer

Background: Tumor stroma percentage (TSP), as an independent, low-cost prognostic factor, could complement current pathology and act as a more feasible risk factor for prognosis. However, TSP hadn't been applied into TNM staging. Here, the objective of our study was to investigate the prognostic significance of TSP in a robust rapid multi-dynamic approach with the application of MATLAB and threshold Algorithm for Gray Image analysis.

Methods: Using a retrospective collection of 1539 CRC patients comprising three independent cohorts; one SGH cohort (N=996) and two validation cohorts (N =106, N= 437) from 2 institutions. We investigated 996 CRC of no special type. According to our established thresholds, 357 cases (35.84%) were classified as TSP-high and 639 cases (64.16%) as TSP-low. We determined the gray image area as the stromal part of the WSI and calculated the stroma percentage with our proposed method on MATLAB software.

Results: In both TSP-cad(50%) and TSP-cad(median), multivariate analysis showed the TSP-cad was an independent prognostic factor for the vessel invasion and tumor location. For OS, TSP-manual HR=1.512 (95% CI 1.045-2.187); TSP-cad HR=1.443 (95% CI 0.993-2.097) and TSP-cad(median) HR=1.632 (95% CI 1.105-2.410). Fortunately, TSP-manual and TSP-cad were also found independent prognostic factor in all the cohorts. It was found that TSP-cad had slightly higher HR and wider CI than TSP-manual.

Conclusions: Our research showed that TSP was an independent prognostic factor in CRC. Moreover, threshold algorithm for the quantitation of TSP could be established. In conclusion, with this Rapid multi-dynamic threshold Algorithm for Gray Image counting of TSP, which showed a higher accuracy than manual evaluation by pathologists and could be a practical method for CRC to guide clinical decision making.

Biomarkers for site-specific response to neoadjuvant chemotherapy in epithelial ovarian cancer: relating MRI changes to tumour cell load and necrosis

BACKGROUND

Diffusion-weighted magnetic resonance imaging (DW-MRI) potentially interrogates site-specific response to neoadjuvant chemotherapy (NAC) in epithelial ovarian cancer (EOC).

METHODS

Participants with newly diagnosed EOC due for platinum-based chemotherapy and interval debulking surgery were recruited prospectively in a multicentre study (n = 47 participants). Apparent diffusion coefficient (ADC) and solid tumour volume (up to 10 lesions per participant) were obtained from DW-MRI before and after NAC (including double-baseline for repeatability assessment in n = 19). Anatomically matched lesions were analysed after surgical excision (65 lesions obtained from 25 participants). A trained algorithm determined tumour cell fraction, percentage tumour and percentage necrosis on histology. Whole-lesion post-NAC ADC and pre/post-NAC ADC changes were compared with histological metrics (residual tumour/necrosis) for each tumour site (ovarian, omental, peritoneal, lymph node).

RESULTS

Tumour volume reduced at all sites after NAC. ADC increased between pre- and post-NAC measurements. Post-NAC ADC correlated negatively with tumour cell fraction. Pre/post-NAC changes in ADC correlated positively with percentage necrosis. Significant correlations were driven by peritoneal lesions.

CONCLUSIONS

Following NAC in EOC, the ADC (measured using DW-MRI) increases differentially at disease sites despite similar tumour shrinkage, making its utility site-specific. After NAC, ADC correlates negatively with tumour cell fraction; change in ADC correlates positively with percentage necrosis.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov NCT01505829.

Optical and magnetic resonance imaging approaches for investigating the tumour microenvironment: state-of-the-art review and future trends

The tumour microenvironment (TME) strongly influences tumorigenesis and metastasis. Two of the most characterized properties of the TME are acidosis and hypoxia, both of which are considered hallmarks of tumours as well as critical factors in response to anticancer treatments. Currently, various imaging approaches exist to measure acidosis and hypoxia in the TME, including magnetic resonance imaging (MRI), positron emission tomography and optical imaging. In this review, we will focus on the latest fluorescent-based methods for optical sensing of cell metabolism and MRI as diagnostic imaging tools applied both in vitro and in vivo. The primary emphasis will be on describing the current and future uses of systems that can measure intra- and extra-cellular pH and oxygen changes at high spatial and temporal resolution. In addition, the suitability of these approaches for mapping tumour heterogeneity, and assessing response or failure to therapeutics will also be covered.

Relationship between MRI findings and invasive breast cancer with podoplanin-positive cancer-associated fibroblasts

OBJECTIVE

Purpose of our study is to assess the relationship between MRI findings and invasive breast cancer (IBC) with cancer-associated fibroblasts (CAFs) that are positive for podoplanin.

METHODS

We retrospectively analyzed the consecutive 109 IBCs. The IBCs were dichotomized as with (+) or without (-) podoplanin-positive CAFs. In MRI analyses, the dichotomized IBCs were compared the lesion to muscle ratio (L/M ratio) in STIR images, the ADC value, the distribution of kinetic parameters, and morphological findings.

RESULTS

Of the 109 IBCs, 28 (26%) IBCs had podoplanin(+) CAFs. Compared to the podoplanin(-) group, the podoplanin(+) group tended to have a more malignant pathological status. In the STIR images, the podoplanin(+) group had significantly higher L/M ratio (7.59 vs. 6.55, p = 0.040). In a dynamic study, the podoplanin(+) group had a significantly higher percentage of the washout pattern (42.21% vs. 29.43%, p = 0.045). There were 23 mass lesions and 5 non-mass enhancement (NME) lesions in the podoplanin(+) group, and 69 mass lesions and 12 NME lesions in the podoplanin(-) group. The mass lesions of the podoplanin(-) group had a significantly higher likelihood of showing an irregular shape (n = 47 vs. 8, p = 0.035). The podoplanin(+) group's lesions had a significantly higher likelihood of showing a circumscribed margin (n = 14 vs. 6, p < 0.001) and a rim enhancement (n = 10 vs. 13, p = 0.047). In multivariate analyses, only high nuclear grade was significant predictive value of podoplanin(+) CAFs.

CONCLUSION

Although not significant in multivariate analyses, MRI findings may be used to determine the podoplanin-positive CAF status of invasive breast cancer.

Correlation of the tumour-stroma ratio with diffusion weighted MRI in rectal cancer

OBJECTIVE

This study evaluated the correlation between intratumoural stroma proportion, expressed as tumour-stroma ratio (TSR), and apparent diffusion coefficient (ADC) values in patients with rectal cancer.

METHODS

This multicentre retrospective study included all consecutive patients with rectal cancer, diagnostically confirmed by biopsy and MRI. The training cohort (LUMC, Netherlands) included 33 patients and the validation cohort (VHIO, Spain) 69 patients. Two observers measured the mean and minimum ADCs based on single-slice and whole-volume segmentations. The TSR was determined on diagnostic haematoxylin & eosin stained slides of rectal tumour biopsies. The correlation between TSR and ADC was assessed by Spearman correlation (r_s).

RESULTS

The ADC values between stroma-low and stroma-high tumours were not significantly different. Intra-class correlation (ICC) demonstrated a good level of agreement for the ADC measurements, ranging from 0.84-0.86 for single slice and 0.86-0.90 for the whole-volume protocol. No correlation was observed between the TSR and ADC values, with ADC_meanr_s= -0.162 (p= 0.38) and ADC_minr_s= 0.041 (p= 0.82) for the single-slice and r_s= -0.108 (p= 0.55) and r_s= 0.019 (p= 0.92) for the whole-volume measurements in the training cohort, respectively. Results from the validation cohort were consistent; ADC_meanr_s= -0.022 (p= 0.86) and ADC_minr_s = 0.049 (p= 0.69) for the single-slice and r_s= -0.064 (p= 0.59) and r_s= -0.063 (p= 0.61) for the whole-volume measurements.

CONCLUSIONS

Reproducibility of ADC values is good. Despite positive reports on the correlation between TSR and ADC values in other tumours, this could not be confirmed for rectal cancer.

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

OBJECTIVE

To evaluate the associations between computer-aided diagnosis (CAD)-generated kinetic volume parameters and survival in triple-negative breast cancer (TNBC) patients.

METHODS

40 patients with TNBC who underwent pre-operative MRI between March 2008 and March 2014 were included. We analyzed CAD-generated parameters on dynamic contrast-enhanced MRI, visual MRI assessment, and histopathological data. Cox proportional hazards models were used to determine associations with survival outcomes.

RESULTS

12 of the 40 (30.0%) patients experienced recurrence and 7 died of breast cancer after a median follow-up of 73.6 months. In multivariate analysis, higher percentage volume (%V) with more than 200% initial enhancement rate correlated with worse disease-specific survival (hazard ratio, 1.12; 95% confidence interval, 1.02-1.22; p-value, 0.014) and higher %V with more than 100% initial enhancement rate followed by persistent curve type at 30% threshold correlated with worse disease-specific survival (hazard ratio, 1.33; 95% confidence interval, 1.10-1.61; p-value, 0.004) and disease-free survival (hazard ratio, 1.27; 95% confidence interval, 1.12-1.43; p-value, 0.000).

CONCLUSION

CAD-generated kinetic volume parameters may correlate with survival in TNBC patients. Further study would be necessary to validate our results on larger cohorts.

ADVANCES IN KNOWLEDGE

CAD generated kinetic volume parameters on breast MRI can predict recurrence and survival outcome of patients in TNBC. Varying the enhancement threshold improved the predictive performance of CAD generated kinetic volume parameter.