Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools

The Oncologist, Journal Year: 2019, Volume and Issue: 25(2), P. e231 - e242

Published: Oct. 14, 2019

Abstract The detection of lymph node metastasis affects the management patients with primary breast cancer significantly in terms staging, treatment, and prognosis. main goal for radiologist is to determine detect presence metastatic disease nonpalpable axillary nodes a positive predictive value that high enough initially select upfront dissection. Features are suggestive adenopathy may be seen different imaging modalities, but ultrasound method choice evaluating performing image-guided interventions. This review aims provide comprehensive overview available modalities assessment diagnosed cancer.

Language: Английский

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Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer

European Radiology, Journal Year: 2019, Volume and Issue: 29(7), P. 3820 - 3829

Published: Jan. 30, 2019

Language: Английский

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Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches

Seminars in Cancer Biology, Journal Year: 2023, Volume and Issue: 96, P. 11 - 25

Published: Sept. 12, 2023

Language: Английский

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Machine learning in breast MRI

Journal of Magnetic Resonance Imaging, Journal Year: 2019, Volume and Issue: 52(4), P. 998 - 1018

Published: July 5, 2019

Machine-learning techniques have led to remarkable advances in data extraction and analysis of medical imaging. Applications machine learning breast MRI continue expand rapidly as increasingly accurate 3D lesion segmentation allows the combination radiologist-level interpretation (eg, BI-RADS lexicon), from advanced multiparametric imaging techniques, patient-level such genetic risk markers. Advances feature rapid dataset analysis, which offers promise large pooled multiinstitutional analysis. The object this review is provide an overview machine-learning deep-learning for MRI, including supervised unsupervised methods, anatomic segmentation, segmentation. Finally, it explores role learning, current limitations, future applications texture radiomics, radiogenomics. Level Evidence: 3 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2019. 2020;52:998-1018.

Language: Английский

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A Deep Look Into the Future of Quantitative Imaging in Oncology: A Statement of Working Principles and Proposal for Change

International Journal of Radiation Oncology*Biology*Physics, Journal Year: 2018, Volume and Issue: 102(4), P. 1074 - 1082

Published: Aug. 28, 2018

Language: Английский

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Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer

Frontiers in Oncology, Journal Year: 2019, Volume and Issue: 9

Published: Sept. 30, 2019

Purpose: To investigate whether a combination of radiomics and automatic machine learning applied to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) primary breast cancer can non-invasively predict axillary sentinel lymph node (SLN) metastasis. Methods: 62 patients who received DCE-MRI scan were enrolled. Tumor resection biopsy performed within 1 week after the examination. According time signal intensity curve, volumes interest (VOIs) delineated on whole tumor in images with strongest enhanced phase. Datasets randomly divided into two sets including training set (~80%) validation (~20%). A total 1,409 quantitative features extracted from each VOI. The select K best least absolute shrinkage selection operator (Lasso) used obtain optimal features. Three classification models based logistic regression (LR), XGboost, support vector (SVM) classifiers constructed. Receiver Operating Curve (ROC) analysis was analyze prediction performance models. Both feature construction firstly set, then further tested by same thresholds. Results: There is no significant difference between all clinical pathological variables without SLN metastasis (P > 0.05), except histological grade = 0.03). Six obtained as for construction. In respect accuracy MSE, SVM demonstrated highest performance, an accuracy, AUC, sensitivity (for positive SLN), specificity SLN) Mean Squared Error (MSE) 0.85, 0.83, 0.71, 1, 0.26, respectively. Conclusions: We feasibility combining artificial intelligence tumors cancer. This non-invasive approach could be very promising application.

Language: Английский

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Development and validation of a magnetic resonance imaging-based model for the prediction of distant metastasis before initial treatment of nasopharyngeal carcinoma: A retrospective cohort study

EBioMedicine, Journal Year: 2019, Volume and Issue: 40, P. 327 - 335

Published: Jan. 11, 2019

We aimed to identify a magnetic resonance imaging (MRI)-based model for assessment of the risk individual distant metastasis (DM) before initial treatment nasopharyngeal carcinoma (NPC).This retrospective cohort analysis included 176 patients with NPC. Using PyRadiomics platform, we extracted features primary tumors in all who did not exhibit DM treatment. Subsequently, used minimum redundancy-maximum relevance and least absolute shrinkage selection operator algorithms select strongest build logistic prediction. The independent statistical significance multiple clinical variables was tested using multivariate regression analysis.In total, 2780 radiomic were extracted. A MRI-based (DMMM) comprising seven constructed classification into high- low-risk groups training validated an cohort. Overall survival significantly shorter high-risk group than (P < 0·001). radiomics nomogram based on developed each patient, it showed significant predictive ability [area under curve (AUC), 0·827; 95% confidence interval (CI), 0.754-0.900] validation (AUC, 0.792; CI, 0.633-0.952) cohorts.DMMM can serve as visual prognostic tool prediction NPC, improve decisions by aiding differentiation high low risks DM. FUND: This research received financial support from National Natural Science Foundation China (81571664, 81871323, 81801665, 81771924, 81501616, 81671851, 81527805); Guangdong Province (2018B030311024); Technology Planning Project (2016A020216020); Scientific Research General Guangzhou Innovation Commission (201707010328); Postdoctoral (2016M600145); Key R&D Program (2017YFA0205200, 2017YFC1308700, 2017YFC1309100).

Language: Английский

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A New Challenge for Radiologists: Radiomics in Breast Cancer

BioMed Research International, Journal Year: 2018, Volume and Issue: 2018, P. 1 - 10

Published: Oct. 8, 2018

Over the last decade, field of medical imaging experienced an exponential growth, leading to development radiomics, with which innumerable quantitative features are obtained from digital images, providing a comprehensive characterization tumor. This review aims assess role this emerging diagnostic tool in breast cancer, focusing on ability radiomics predict malignancy, response neoadjuvant chemotherapy, prognostic factors, molecular subtypes, and risk recurrence.

Language: Английский

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Preoperative prediction of pelvic lymph nodes metastasis in early-stage cervical cancer using radiomics nomogram developed based on T2-weighted MRI and diffusion-weighted imaging

European Journal of Radiology, Journal Year: 2019, Volume and Issue: 114, P. 128 - 135

Published: March 20, 2019

Language: Английский

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Radiomics analysis of magnetic resonance imaging improves diagnostic performance of lymph node metastasis in patients with cervical cancer

Radiotherapy and Oncology, Journal Year: 2019, Volume and Issue: 138, P. 141 - 148

Published: June 25, 2019

Language: Английский

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