Abstract 4035
Background
Breast cancer is one of the most common malignant disease for women. Mammography is the preferred method for breast cancer detection. The purpose is to investigate the feasibility and accuracy of texture features extracted from digital mammograms at predicting benign and malignant breast mass using Radiomics.
Methods
494 digital mammograms data who diagnosed as breast masses (Benign: 251 Malignant: 243) by mammography were enrolled. Enrol criteria: breast masses classified as BI-RADS 3, 4, and 5 and at last confirmed by histopathology. Lesion area was marked with a rectangular frame on the Cranio-Caudal (CC) and MedioLateral Oblique (MLO) images at the 5M workstation. The rectangular regions of interest (ROI) was segmented and 456 radiomics features were extracted from every ROI. Extracted features were dimensioned by Maximum Relevance Minimum Redundancy (MRMR) and Lasso algorithm. Post-dimension features were classified using Support Vector Machine (SVM). 70% of the data as a training set and the other 30% as a testing set. The reliability of the Classifier was evaluated by the 10-fold cross-validation. The classification accuracy was evaluated by the accuracy and sensitivity and AUC.
Results
Both the MRMR and Lasso screened 30 radiomics features respectively. 10-fold cross-validation showed that their accuracy were 88.70% and 86.71%, respectively. In testing sets, Through the MRMR algorithm, the classifier achieves an accuracy of 92.00% and a sensitivity of 91.10% and AUC of 95.10%. Through the lasso dimension reduction algorithm, the classifier achieves an accuracy of 83.26% and a sensitivity of 75.90% and AUC of 89.38%.
Conclusions
Radiomics texture features from digital mammograms may be used for benign and malignant prediction. This method offer better accuracy and sensitivity. It is expected to provide an auxiliary diagnosis for the imaging doctors.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Has not received any funding.
Disclosure
All authors have declared no conflicts of interest.
Resources from the same session
3264 - A novel preclinical model of RAF-independent MEK1 mutant tumors and its treatment with novel ATP competitive MEK inhibitor
Presenter: Luca Hegedus
Session: Poster Display session 3
Resources:
Abstract
4918 - HER2 inhibition in Aggressive Squamous Cell Carcinomas driven by a common MET Sema Domain Polymorphism
Presenter: Nur Afiqah Mohamed Salleh
Session: Poster Display session 3
Resources:
Abstract
2426 - ADAM9 as a target for lung cancer treatment
Presenter: Yuh-pyng Sher
Session: Poster Display session 3
Resources:
Abstract
5537 - Novel polyurea/polyurethane nanocapsules loaded with a tambjamine analog to improve cancer chemotherapy delivery and safety in lung cancer
Presenter: Marta Perez Hernandez
Session: Poster Display session 3
Resources:
Abstract
1597 - Discovery of Clinical Candidate DBPR112, a Furanopyrimidine-based Epidermal Growth Factor Receptor Inhibitor for the Treatment of Non-Small Cell Lung Cancer
Presenter: Hsing-pang Hsieh
Session: Poster Display session 3
Resources:
Abstract
3543 - Molecular characteristics in lung squamous cell carcinomas dependent on TP53 status – putative targets
Presenter: Vilde Haakensen
Session: Poster Display session 3
Resources:
Abstract
4111 - Comparison of molecular profiles between primary tumour and matched metastasis in non-small cell lung cancer
Presenter: Asuka Kawachi
Session: Poster Display session 3
Resources:
Abstract
4559 - Treatment with BLU-667, a potent and selective RET inhibitor, provides rapid clearance of ctDNA in Patients with RET-altered Non-Small Cell Lung Cancer (NSCLC) and Thyroid Cancer
Presenter: Giuseppe Curigliano
Session: Poster Display session 3
Resources:
Abstract
2501 - Triple MET/SRC/PIM inhibition in MET addicted tumors
Presenter: Ilaria Attili
Session: Poster Display session 3
Resources:
Abstract
5655 - Bioactivation of napabucasin triggers reactive oxygen species–mediated cancer cell death
Presenter: Fieke Froeling
Session: Poster Display session 3
Resources:
Abstract