Association between survival in patients with primary invasive breast cancer and computer aided MRI

Dietzel, Matthias; Zoubi, R; Vag, Tibor; Gajda, Mieczysław; Runnebaum, Ingo B.; Kaiser, Werner A.; Baltzer, Pascal

doi:10.1002/jmri.23812

Cited by 25 publications

(34 citation statements)

References 39 publications

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“…In our opinion, this indicates its potential to further improve the predictive accuracy of bMRI. This is also in accordance with previous research on prognostic bMRI, where VAV results were identified as tissue biomarkers and surrogates of patient outcome in breast cancer [6][7][8].…”

Section: Discussionsupporting

confidence: 77%

“…Logistic regression identified typical VAV patterns for CG1 (VAV 0 : odds ratio (OR) = 20.9) and CG4 (VAV 4 : OR = 33.4; VAV 6 : OR = 66.8; all p < 0.001). Figure 2 summarizes the association of ΔTD, ΔTV VAV with CG.…”

Section: Vavmentioning

confidence: 99%

“…As shown in previous research, such data reflect the tumor biology, are connected to patient outcomes [6][7][8], and could be used as surrogate parameters of TR (for instance [9]). Yet, the major drawback of bMRI is its complex interpretation [10,11].…”

Section: Introductionmentioning

confidence: 99%

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Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy

et al. 2017

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Background: We aimed to investigate an automated semi-quantitative software as an imaging biomarker for the prediction of tissue response (TR) after completion of neoadjuvant chemotherapy (NAC). Methods: Breast magnetic resonance imaging (MRI) (1.5T, protocol according to international recommendations) of 67 patients with biopsy-proven invasive breast cancer were examined before and after NAC. After completion of NAC, histopathologic assessments of TR were classified according to the Chevallier grading system (CG1/4: full/non-responder; CG2/C3: partial responder). A commercially available fully automatic software (CADstream) extracted MRI parameters of tumor extension (tumor diameter/volume: TD/TV). Pre- versus post-NAC values were compared (ΔTV and ΔTD). Additionally, the software performed volumetric analyses of vascularization (VAV) after NAC. Accuracy of MRI parameters to predict TR were identified (cross-tabs, ROC, AUC, Kruskal-Wallis). Results: There were 37 (34.3%) CG1, 7 (6.5%) CG2, 53 (49.1%) CG3, and 11 (10.2%) CG4 lesions. The software reached area under the curve levels of 79.5% (CG1/complete response: ΔTD), 68.6% (CG2, CG3/partial response: VAV), and 88.8% to predict TR (CG4/non-response: ΔTV). Conclusion: Semi-quantitative automated analysis of breast MRI data enabled the prediction of tissue response to NAC.

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Section: Discussionsupporting

confidence: 77%

Section: Vavmentioning

confidence: 99%

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Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy

et al. 2017

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“…In computer-aided breast cancer detection/diagnosis software, an R% cutoff threshold value of 30% or 50% was clinically observed 24 . The lower cutoff value of 20% reported here, may reflect specific/different properties associated with characterizing the contrast enhancement on normal breast tissues for studying breast cancer risk.…”

Section: Discussionmentioning

confidence: 99%

DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence

Zuley

Berg

et al. 2017

Sci Rep

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We investigated automated quantitative measures of background parenchymal enhancement (BPE) derived from an early versus delayed post-contrast sequence in breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for association with breast cancer presence in a case-control study. DCE-MRIs were retrospectively analyzed for 51 cancer cases and 51 controls with biopsy-proven benign lesions, matched by age and year-of-MRI. BPE was quantified using fully-automated validated computer algorithms, separately from three sequential DCE-MRI post-contrast-subtracted sequences (SUB1, SUB2, and SUB3). The association of BPE computed from the three SUBs and other known factors with breast cancer were assessed in terms of odds ratio (OR) and area under the receiver operating characteristic curve (AUC). The OR of breast cancer for the percentage BPE measure (BPE%) quantified from SUB1 was 3.5 (95% Confidence Interval: 1.3, 9.8; p = 0.015) for 20% increments. Slightly lower and statistically significant ORs were also obtained for BPE quantified from SUB2 and SUB3. There was no significant difference (p > 0.2) in AUC for BPE quantified from the three post-contrast sequences and their combination. Our study showed that quantitative measures of BPE are associated with breast cancer presence and the association was similar across three breast DCE-MRI post-contrast sequences.

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“…Thus, we hypothesized that the f value from IVIM modeling may correlate with quantitative kinetic features from a DCE‐MRI using computer‐aided diagnosis (CAD). Furthermore, several CAD‐assessed kinetic features, such as a higher washout component and a higher peak enhancement of the tumor at preoperative MRI, are known as poor survival indicators in breast cancer patients . Demonstrating the relationship between CAD‐assessed kinetic features and IVIM parameters could be helpful in characterizing complex cancerous tissue and in predicting prognosis in breast cancer patients.…”

mentioning

confidence: 99%

Intravoxel incoherent motion diffusion‐weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer‐aided diagnosis

Song

Cho

Seo

et al. 2018

Magnetic Resonance Imaging

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Background As both intravoxel incoherent motion (IVIM) modeling and dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) provide perfusion parameters, IVIM‐derived perfusion parameters might be expected to correlate with the kinetic features from DCE‐MRI. Purpose To investigate the association between IVIM parameters and prognostic factors and to evaluate the correlation between IVIM parameters and kinetic features in invasive breast cancer patients using computer‐aided diagnosis (CAD). Study Type Retrospective. Population Eighty‐five patients (invasive cancers; mean size, 1.8 cm; range, 0.8–4.8 cm) who underwent diffusion‐weighted imaging with 12 b‐values (0–1000 s/mm2). Field Strength/Sequence 3.0T MRI axial, IVIM‐DWI epi‐sequence, and DCE‐MRI. Assessment Two radiologists measured the apparent diffusion coefficient (ADC), diffusion coefficient, pseudodiffusion coefficient, and perfusion fraction (f) using IVIM modeling. Kinetic features such as peak enhancement and early and delayed enhancement profiles were acquired using CAD. Statistical Tests The correlation between the IVIM parameters and kinetic features and the association between the IVIM parameters and prognostic factors were investigated using Mann–Whitney test and Spearman correlation test. Results There were no significant associations between IVIM parameters and prognostic factors. When IVIM parameters were correlated with kinetic features by CAD, both the ADC and f values showed correlations with delayed enhancement profiles. The ADC values were lower in tumors with lower persistent components (P = 0.013) and higher washout components (P = 0.045) and showed a positive correlation with persistent proportion (Spearman's rho (r) = 0.222, P = 0.041). The f value was higher in tumors with higher persistent components (P = 0.021) and showed a positive correlation with persistent proportion (r = 0.227, P = 0.029). Data Conclusion This analysis revealed that IVIM‐derived ADC and f values showed correlations with kinetic features at the delayed phase as assessed by CAD. These results indicate the potential of IVIM imaging biomarkers to provide information on the biological and kinetic properties of breast cancers without a contrast agent. Level of Evidence: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:118–130.

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Association between survival in patients with primary invasive breast cancer and computer aided MRI

Cited by 25 publications

References 39 publications

Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy

Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy

DCE-MRI Background Parenchymal Enhancement Quantified from an Early versus Delayed Post-contrast Sequence: Association with Breast Cancer Presence

Intravoxel incoherent motion diffusion‐weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer‐aided diagnosis

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