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
5105 - Fresh blood Immune cell monitoring in patients treated with nivolumab in the GETUG-AFU26 NIVOREN study: association with toxicity and treatment outcome
Presenter: Aude DESNOYER
Session: Poster Display session 3
Resources:
Abstract
1877 - Advanced clear-cell renal cell carcinoma (accRCC): association of microRNAs (miRNAs) with molecular subtypes, mRNA targets and outcome.
Presenter: Annelies Verbiest
Session: Poster Display session 3
Resources:
Abstract
5543 - Prior tyrosine kinase inhibitors (TKI) and antibiotics (ATB) use are associated with distinct gut microbiota ‘guilds’ in renal cell carcinoma (RCC) patients
Presenter: Valerio Iebba
Session: Poster Display session 3
Resources:
Abstract
2689 - mTOR mutations are not associated with shorter PFS and OS in patients treated with mTOR inhibitors
Presenter: Cristina Suarez Rodriguez
Session: Poster Display session 3
Resources:
Abstract
3069 - Efficacy of immune checkpoint inhibitors (ICI) and genomic alterations by body mass index (BMI) in Advanced Renal Cell Carcinoma (RCC)
Presenter: Aly-Khan Lalani
Session: Poster Display session 3
Resources:
Abstract
5089 - Finding the Right Biomarker for Renal Cell Carcinoma (RCC): Nivolumab treatment induces the expression of specific peripheral lymphocyte microRNAs in patients with durable and complete response.
Presenter: Lorena Incorvaia
Session: Poster Display session 3
Resources:
Abstract
2594 - Algorithms derived from quantitative pathology can be a gatekeeper in patient selection for clinical trials in localised clear cell renal cell carcinoma (ccRCC)
Presenter: In Hwa Um
Session: Poster Display session 3
Resources:
Abstract
2566 - High baseline blood volume is an independent favorable prognostic factor for overall and progression-free survival in patients with metastatic renal cell carcinoma
Presenter: Aska Drljevic-nielsen
Session: Poster Display session 3
Resources:
Abstract
2675 - Impact of estimand selection on adjuvant treatment outcomes in renal cell carcinoma (RCC)
Presenter: Daniel George
Session: Poster Display session 3
Resources:
Abstract
1541 - TERT gene fusions characterize a subset of metastatic Leydig cell tumors
Presenter: Bozo Kruslin
Session: Poster Display session 3
Resources:
Abstract