Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts                     Recurrence-free Survival in Locally Advanced Breast Cancer Treated with                     Neoadjuvant Chemotherapy

Wu, Jia; Cao, Guohong; Sun, Xiaoli; Lee, Juheon; Rubin, Daniel L.; Napel, Sandy; Kurian, Allison W.; Daniel, Bruce L.; Li, Ruijiang

doi:10.1148/radiol.2018172462

Cited by 126 publications

(91 citation statements)

References 46 publications

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“…This informs the importance of the heterogeneity of tumor contrast enhancements and the multiphase DCE sequences in breast MRI. In fact, the utilities of multiple temporal postcontrast sequence and kinetics‐related measures have been shown in previous work . Our study on differentiating the ALN metastasis status adds an example showing the value of kinetic features extracted from multiple postcontrast sequences.…”

Section: Discussionsupporting

confidence: 64%

“…In fact, the utilities of multiple temporal postcontrast sequence and kinetics-related measures have been shown in previous work. [27][28][29] Our study on differentiating the ALN metastasis status adds an example showing the value of kinetic features extracted from multiple postcontrast sequences. Interestingly, when we combined all the radiomic features of the four sequences and the kinetic features (ie, the union), it did not lead to improved performance than using CE2 plus the kinetic features.…”

Section: Discussionmentioning

confidence: 94%

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Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

Chai

et al. 2019

Magnetic Resonance Imaging

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Background The axillary lymph node status is critical for breast cancer staging and individualized treatment planning. Purpose To assess the effect of determining axillary lymph node (ALN) metastasis by breast MRI‐derived radiomic signatures, and compare the discriminating abilities of different MR sequences. Study Type Retrospective. Population In all, 120 breast cancer patients, 59 with ALN metastasis and 61 without metastasis, all confirmed by pathology. Field Strength/Sequence 3 .0T scanner with T1‐weighted imaging, T2‐weighted imaging, diffusion‐weighted imaging, and dynamic contrast‐enhanced (DCE) sequences. Assessment Typical morphological and texture features of the segmented tumor were extracted from four sequences, ie, T1WI, T2WI, DWI, and the second postcontrast phase (CE2) of the dynamic contrast‐enhanced sequences. Additional contrast enhancement kinetic features were extracted from all DCE sequences (one pre‐ and seven postcontrast phases). Linear discriminant analysis classifiers were built and compared when using features from an individual sequence or the combination of the sequences in differentiating the ALN metastasis status. Statistical Tests Mann–Whitney U‐test, Fisher's exact test, least absolute shrinkage selection operator (LASSO) regression, and receiver operating characteristic analysis were performed. Results The accuracy/AUC of the four sequences was 79%/0.87, 77%/0.85, 74%/0.79, and 79%/0.85 for the T1WI, CE2, T2WI, and DWI, respectively. When CE2 was augmented by adding kinetic features, the model achieved the highest performance (accuracy = 0.86 and AUC = 0.91). When all features from the four sequences and the kinetics were combined, it did not lead to a further increase in the performance (P = 0.48). Data Conclusion Breast tumor's radiomic signatures from preoperative breast MRI sequences are associated with the ALN metastasis status, where CE2 phase and the contrast enhancement kinetic features lead to the highest classification effect. Level of Evidence 3 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2019;50:1125–1132.

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Section: Discussionsupporting

confidence: 64%

Section: Discussionmentioning

confidence: 94%

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

Chai

et al. 2019

Magnetic Resonance Imaging

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“…They were found to be associated with tumor-infiltrating lymphocytes in breast cancer, which is a promising predictive biomarker for the effectiveness of immunotherapy treatment [67]. Some of them were identified as valuable prognostic markers for adjuvant and neoadjuvant chemotherapies [68,69]. As a necessary next step for our current work, we will study the corresponding image feature extraction methods as well as imaging-based disease diagnosis and treatment plan selection in the future.…”

Section: Discussionmentioning

confidence: 98%

AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

et al. 2020

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Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting mass-centered image patches manually or automatically. However, manual patch extraction is time-consuming and automatic patch extraction brings errors that could not be compensated in the following segmentation step. In this study, we propose a novel attention-guided dense-upsampling network (AUNet) for accurate breast mass segmentation in whole mammograms directly. In AUNet, we employ an asymmetrical encoder-decoder structure and propose an effective upsampling block, attention-guided dense-upsampling block (AU block). Especially, the AU block is designed to have three merits. Firstly, it compensates the information loss of bilinear upsampling by dense upsampling. Secondly, it designs a more effective method to fuse high-and low-level features. Thirdly, it includes a channel-attention function to highlight rich-information channels. We evaluated the proposed method on two publicly available datasets, CBIS-DDSM and INbreast. Compared to three state-of-the-art fully convolutional networks, AUNet achieved the best performances with an average Dice similarity coefficient of 81.8% for CBIS-DDSM and 79.1% for INbreast.

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“…Clinical breast examinations and radiological studies are established as essential tools for early detection and are associated with significant improvements in patient outcomes 4,6–8. According to the 5th edition of Breast Imaging Reporting and Data System (BI-RADS) classification, there are BI-RADS 1–6 for tumor diagnosis 9.…”

Section: Introductionmentioning

confidence: 99%

Diagnostic value of PD-1 mRNA expression combined with breast ultrasound in breast cancer patients

Fang

Shao

et al. 2018

TCRM

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IntroductionThis study explored the value of measuring programmed death 1 (PD-1) in peripheral blood, combined with breast ultrasound using the Breast Imaging Reporting and Data System (BI-RADS) classification, for differentiation between benign and malignant breast tumors.Materials and methodsWe enrolled 113 patients with breast cancer and 66 patients with benign breast tumors who were admitted to Hangzhou First People’s Hospital from September 2014 to August 2017. The mRNA level of PD-1 was detected by quantitative real-time polymerase chain reaction.ResultsThe mRNA levels of PD-1 were significantly higher in the peripheral blood of patients with breast cancer than those in patients with benign breast tumors. The diagnostic sensitivity of PD-1 mRNA expression was 0.805, the specificity was 0.788, and the area under the curve (AUC) was 0.848 (P < 0.001); the sensitivity of breast ultrasound-based BI-RADS classification was 0.752, the specificity was 0.909, and the AUC was 0.906 (P < 0.001); and the combined sensitivity, specificity, and AUC of the two assays were 0.920, 0.879, and 0.938, respectively (P < 0.001). Progesterone receptor-positive breast cancer patients exhibited high levels of PD-1 expression (P < 0.001).ConclusionThis study suggests that the measurement of PD-1 combined with breast ultrasound-based BI-RADS classification represents a significant improvement for breast cancer diagnosis compared with diagnoses based on either method alone.

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Intratumoral Spatial Heterogeneity at Perfusion MR Imaging Predicts Recurrence-free Survival in Locally Advanced Breast Cancer Treated with Neoadjuvant Chemotherapy

Cited by 126 publications

References 46 publications

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

Differentiating axillary lymph node metastasis in invasive breast cancer patients: A comparison of radiomic signatures from multiparametric breast MR sequences

AUNet: attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms

Diagnostic value of PD-1 mRNA expression combined with breast ultrasound in breast cancer patients

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