Dataset Information

Prognostic significance of microinvasion with ductal carcinoma in situ of the breast: a meta-analysis.

ABSTRACT:

Purpose

Ductal carcinoma in situ (DCIS) associated with invasive carcinoma ≤ 1 mm in size is defined as DCIS with microinvasion (DCIS/microinvasion) rather than as invasive breast carcinoma. The number of patients with microinvasion accounts for < 1% of all breast cancer in published studies. As the numbers are limited, the prognostic significance of DCIS/microinvasion has not been clearly elucidated. This meta-analysis aimed to investigate the survival differences between patients with DCIS/microinvasion and those with pure DCIS.

Methods

A meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was performed. We searched three electronic databases (MEDLINE, Cochrane Library, and EMBASE) and included observational studies published in English that contained survival details of patients with either DCIS or DCIS/microinvasion.

Results

This study identified 26 studies that described the clinicopathological characteristics of patients in both the DCIS and DCIS/microinvasion groups. Survival differences were evaluated in 10 of 26 studies. Disease-free survival and loco-regional recurrence-free survival were significantly shorter in patients with DCIS/microinvasion than in those with DCIS (Hazard ratio, 1.52; 95% confidence interval, 1.11-2.08; p = 0.01 and hazard ratio, 2.53; 95% confidence interval, 1.45-4.41; p = 0.001, respectively). Both overall survival and distant metastasis-free survival tended to be shorter in patients with DCIS/microinvasion than in patients with DCIS (Hazard ratio, 1.63; 95% CI, 0.63-4.23; p = 0.31 and hazard ratio, 1.85; 95% confidence interval, 0.74-4.66; p = 0.19, respectively) but the difference was not statistically significant.

Conclusion

Our meta-analysis suggests that DCIS/microinvasion may display more aggressive biological and clinical behavior than pure DCIS, highlighting the potential need for closer follow-up and consideration of adjuvant treatment strategies in DCIS patients with microinvasive disease.

SUBMITTER: Shiino S

PROVIDER: S-EPMC9823049 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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Publications

Prognostic significance of microinvasion with ductal carcinoma in situ of the breast: a meta-analysis.

Shiino Sho S Quinn Cecily C Ball Graham G Syed Binafsha M BM Kurozumi Sasagu S Tsuda Hitoshi H Rakha Emad A EA

Breast cancer research and treatment 20221124 2

<h4>Purpose</h4>Ductal carcinoma in situ (DCIS) associated with invasive carcinoma ≤ 1 mm in size is defined as DCIS with microinvasion (DCIS/microinvasion) rather than as invasive breast carcinoma. The number of patients with microinvasion accounts for < 1% of all breast cancer in published studies. As the numbers are limited, the prognostic significance of DCIS/microinvasion has not been clearly elucidated. This meta-analysis aimed to investigate the survival differences between patients with ...[more]

PMID: 36427119

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Project description:BackgroundThe role of different subtypes of tumour infiltrating lymphocytes (TILs) in breast ductal carcinoma in situ (DCIS) is still poorly defined. This study aimed to assess the prognostic significance of B and T lymphocytes and immune checkpoint proteins expression in DCIS.MethodsA well characterised DCIS cohort (n = 700) with long-term follow-up comprising pure DCIS (n = 508) and DCIS mixed with invasive carcinoma (IBC; n = 192) were stained immunohistochemically for CD20, CD3, CD4, CD8, FOXP3, PD1 and PDL1. Copy number variation and TP53 mutation status were assessed in a subset of cases (n = 58).ResultsCD3+ lymphocytes were the predominant cell subtype in the pure DCIS cohort, while FOXP3 showed the lowest levels. PDL1 expression was mainly seen in the stromal TILs. Higher abundance of TILs subtypes was associated with higher tumour grade, hormone receptor negativity and HER2 positivity. Mutant TP53 variants were associated with higher levels of stromal CD3+, CD4+ and FOXP3+ cells. DCIS coexisting with invasive carcinoma harboured denser stromal infiltrates of all immune cells and checkpoint proteins apart from CD4+ cells. Stromal PD1 was the most differentially expressed protein between DCIS and invasive carcinoma (Z = 5.8, p < 0.0001). Dense TILs, stromal FOXP3 and PDL1 were poor prognostic factors for DCIS recurrence, while dense TILs were independently associated with poor outcome for all recurrences (HR = 7.0; p = 0.024), and invasive recurrence (HR = 2.1; p = 0.029).ConclusionsImmunosuppressive proteins are potential markers for high risk DCIS and disease progression. Different stromal and intratumoural lymphocyte composition between pure DCIS, DCIS associated with IBC and invasive carcinoma play a potential role in their prognostic significance and related to the underlying genomic instability. Assessment of overall TILs provides a promising tool for evaluation of the DCIS immune microenvironment.

Project description:BackgroundThe treatment and prognosis of breast ductal carcinoma in situ (DCIS) with and without microinvasion (MIC) are different. Ultrasound imaging shows that DCIS is a heterogeneous breast tumor with diverse manifestations. DCIS means that the cancer cells are confined in the duct without penetrating the basement membrane, MIC means that the cancer cells penetrate the basement membrane and the maximum diameter of any largest invasive lesion is less than or equal to 1 mm. This study was designed to evaluate how deep learning can be used to identify DCIS with MIC on ultrasound images.MethodsThe clinical and ultrasound data of 467 consecutive inpatients diagnosed with DCIS (213 with MIC) in West China Hospital of Sichuan University were collected from January 2013 to April 2019 and randomly apportioned to training and internal validation sets. An external validation set comprised data from Sichuan Provincial People's Hospital with 101 patients (33 with MIC) collected between January 2017 and December 2019. There were 2,492 original images; 66% of these were used to establish a model, and the remaining 34% were used to evaluate the model. Three experienced breast ultrasound clinicians analyzed the ultrasound images to establish a logistic regression model. Finally, the logistic regression model and five deep learning models (ResNet-50, ResNet-101, DenseNet-161, DenseNet-169, and Inception-v3) were compared and evaluated to assess their diagnostic efficiency when identifying MIC based on ultrasound image data.ResultsThe characteristics of high nuclear grade (P<0.001), necrosis (P=0.006), estrogen receptor negative (ER-; P=0.003), progesterone receptor negative (PR-; P=0.001), human epidermal growth factor receptor 2 positive (HER2+; P=0.034), lymphatic metastasis (P=0.008), and calcification (P<0.001) all showed significant correlations with MIC. The Inception-v3 model achieved the best performance (P<0.05) in MIC identification. The area under the receiver operating curve (AUC) of the Inception-v3 model was 0.803 [95% confidence interval (CI): 0.709 to 0.878], with a classification accuracy of 0.766, a sensitivity of 0.767, and a specificity of 0.765.ConclusionsDeep learning can be used to identify MIC of breast DCIS from ultrasound images. Models based on Inception-v3 can provide automated detection of DCIS with MIC from ultrasound images.

Dataset Information

Prognostic significance of microinvasion with ductal carcinoma in situ of the breast: a meta-analysis.

Purpose

Methods

Results

Conclusion

Publications

Prognostic significance of microinvasion with ductal carcinoma in situ of the breast: a meta-analysis.

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