ArticleScientific reports2018
Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 32 citations in OpenAlex.
- The Convergence of Radiology and Genomics: Advancing Breast Cancer Diagnosis with Radiogenomics.Cancers · 2024Review
- Volumetric measurement of paranasal sinuses and its clinical significance in pituitary neuroendocrine tumors operated using an endoscopic endonasal approach.Frontiers in neurology · 2023Article
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions.Methods and protocols · 2022Review
- Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.Journal of applied clinical medical physics · 2022Article
- Radiomics and radiogenomics in ovarian cancer: a literature review.Abdominal radiology (New York) · 2021Review
- Radiomics feature robustness as measured using an MRI phantom.Scientific reports · 2021Article
- Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients.European radiology · 2021Article
- Radiomics in medical imaging-"how-to" guide and critical reflection.Insights into imaging · 2020Review
- A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy.Breast cancer research : BCR · 2020Article
- The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology.Frontiers in oncology · 2020Review
- MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors.Neuro-oncology · 2019Article
- Dynamic multiatlas selection-based consensus segmentation of head and neck structures from CT images.Medical physics · 2019Article
- AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics.European journal of nuclear medicine and molecular imaging · 2019Review
- Reliability of tumor segmentation in glioblastoma: Impact on the robustness of MRI-radiomic features.Medical physics · 2019Article
- Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis.Magnetic resonance in medicine · 2019Article
- Multiple Resolution Residually Connected Feature Streams for Automatic Lung Tumor Segmentation From CT Images.IEEE transactions on medical imaging · 2019Article
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
We present a segmentation approach that combines GrowCut (GC) with cancer-specific multi-parametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2- (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). GCGMM's segmentations and the texture features computed from those segmentations were the most reproducible compared with manual delineations and other analyzed segmentation methods. Finally, random forest (RF) classifier trained with leave-one-out cross-validation using features extracted from GCGMM segmentation resulted in the best accuracy for ER-HER2+ vs. ERPR+/TN (GCGMM 0.95, expert 0.95, GC 0.90, FCM 0.92) and for ERPR + HER2- vs. TN (GCGMM 0.92, expert 0.91, GC 0.77, FCM 0.83).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.