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Prediction of benign and malignant breast masses using digital mammograms texture features
abstracts
Annals of Oncology Disclosure: P. Heudel: Research grant / Funding (institution): AstraZeneca; Honoraria (institution): novartis; Honoraria (self): Pfizer. O. Tredan: Honoraria (self): Roche; Honoraria (self): Pfizer; Honoraria (self): novartis; Honoraria (self): AstraZeneca; Honoraria (self): lilly; Honoraria (self): BMS; Honoraria (self): MSD. All other authors have declared no conflicts of interest.
1428P
Circulating tumour cell detection in epithelial ovarian cancer using dual-component antibodies targeting EpCAM and FRa
Background: Circulating tumor cell (CTC) detection methods based on epithelial cell adhesion molecule (EpCAM) have low detection rates in epithelial ovarian cancer (EOC). Meanwhile, folate receptor alpha (FRa) has high expression in EOC cells. We explored the feasibility of combining FRa and EpCAM as CTC capture targets in EOC. Methods: EpCAM and FRa antibodies were linked to magnetic nanospheres (MNs) using the principle of carbodiimide chemistry. Blood samples from healthy donor spiked with A2780 ovarian cancer cells were used for detecting the capture rate. Ninetyfive blood samples from 30 patients with EOC were used for comparing the positive rate of detection when using anti-EpCAM-MNs alone with that when using combination of anti-EpCAM-MNs and anti-FRa-MNs. Samples from 28 patients initially diagnosed with EOC and who did not undergo any treatment and 20 patients with ovarian benign disease were used for evaluating the sensitivity and specificity of combination of anti-EpCAM-MNs and anti-FRa-MNs. Results: Regression analysis between the number of recovered and that of spiked A2780 cells revealed yEpCAM ¼ 0.535x (R2 ¼ 0.99), yFRa ¼ 0.901x (R2 ¼ 0.99) and yEpCAMþFRa ¼ 0.928x (R2 ¼ 0.99). In mixtures of A2780 and MCF7 cells, the capture rate was 92% using the combination of anti-EpCAM-MNs and anti-FRa-MNs, exceeding the rate when using anti-EpCAM-MNs or anti-FRa-MNs alone by approximately 20% (P < 0.01). The combination of anti-EpCAM-MNs and anti-FRa-MNs showed significantly increased positive rate compared with anti-EpCAM-MNs alone (v2 ¼ 14.45, P < 0.001). Sensitivity values were 0.536 and 0.75 when using anti-EpCAM-MNs alone and when using the combination of anti-EpCAM-MNs and anti-FRa-MNs, respectively. Specificity values were 0.9 and 0.85, respectively. The combination of antiEpCAM-MNs and anti-FRa-MNs improved the sensitivity of CTC detection in patients with newly diagnosed EOC (v2 ¼ 4.17; P ¼ 0.041). Conclusions: The combination of FRa and EpCAM is feasible as a CTC capture target of CTC detection in patients with EOC. Legal entity responsible for the study: The authors. Funding: The NNSFC (National Natural Science Foundation of China) (81802980, 81770169, 81670144) and the Health Committee Research Project Fund of Hubei Province (WJ2019M179). Disclosure: All authors have declared no conflicts of interest.
1429P
CEUS of the breast: Is it feasible in improved performance of BI-RADS evaluation of critical breast lesions? A multi-center prospective study in China
J. Luo Ultrasound, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China Background: Ultrasound is the first and primary breast screening for thoes women with small and dense breast and is superior to mammography. BI-RADS using ultrasound causes approxinately over 40%-60% false-positive results, unnecessary biopsy and relatively low cancer-to biosy rate. This multi-center study in China is to determine whether contrastenhanced ultrasound (CEUS) of the breast can improve the precision of BI-RADS. Methods: 1721 patients were enrolled at 8 sites in China. CEUS was performed before core needle biopsy or surgical resection and a revised BI-RADS classification was assigned based on CEUS performance. Using pathological results as golden standerd to evaluate the diagnostic performance of CEUS-based BI-RADS. Results: 1738 solid breast lesions (5.0-39.8mm, 17.896 8.65mm) classified as BI-RADS 4 or 5 on conventional ultrasound or mammography. 771/1738(44.36%) are malignant and 967/1738(55.64%) are benign. The CEUS-based BI-RADS evaluation classified 402/1738 (23.13%) lesions into category 3 and its accuracy, sensitivity, specificity, positive and negative predictive values of 65.0%, 97.0%, 40.0%, 56.0% and 94.0%. The cancer-to-biopsy yield was 57.71% with CEUS-based BI-RADS 3 selected as the biopsy threshold compared with 44.36% otherwise, while the total biopsy rate was only 76.87% compared with 100% otherwiseand will reduce 39.5% (382/967) unnecessary biopsy rate in those benign nodules. Overall, only 2.59% of invasive cancers were misdiagnosed similar as BI-RADS 3 we use nowadays. Conclusions: This study suggests that evaluation of BI-RADS 4 or 5 breast lesions with CEUS result in reduced biopsy rates and increased cancer-to-biopsy yields. Legal entity responsible for the study: Jun Luo. Funding: Has not received any funding. Disclosure: The author has declared no conflicts of interest.
Volume 30 | Supplement 5 | October 2019
Classification of abnormal findings on ring-type dedicated breast PET for detecting breast cancer
S. Sasada, N. Masumoto, M. Nishina, Y. Kimura, A. Amioka, T. Itagaki, A. Emi, T. Kadoya, M. Okada Surgical Oncology, Institute of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan Background: Ring-type dedicated breast positron emission tomography (DbPET) can detect small breast cancers; however, there are no category classifications of abnormal findings on DbPET such as BI-RADs (mammography, ultrasonography, and magnetic resonance imaging). We investigated whether the classification of DbPET findings was useful for detecting breast cancer. Methods: A total of 674 patients with breast cancers underwent ring-type DbPET using FDG before treatment between January 2016 and March 2019. Findings were morphologically categorized as a focus (uptake size 5 mm), mass (>6 mm), or non-mass (multiple uptakes). Non-mass uptakes were additionally classified based on the distribution: focal, linear, regional, segmental, and diffuse. Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (TNR) were calculated. The final diagnosis was pathologically evaluated based on biopsy or surgical specimens, and lesions of category 2 or lower by conventional examinations were determined benign. Results: Among 867 abnormal findings, 668 (77%) were malignant and 199 (23%) were benign. Morphologically, 187 (21.6%) lesions were foci, 413 (47.6%) were masses, and 267 (30.8%) were non-masses. Among non-mass lesions, 131 focal, 1 linear, 15 regional, 115 segmental, and 5 diffuse distributions were presented. The median SUVmax was 5.0 and TNR was 2.8. The area under the curve values of SUVmax and TNR for predicting malignancy were 0.824 and 0.855, respectively. In a multivariate analysis, mass, focal and segmental distributions of non-mass lesions, high TNR were significantly related with breast cancer (all P < 0.001). Pathologically confirmed benign lesions included 45 mastopathies, 29 papillomas, 10 fibroadenomas, 7 ductal adenomas, and 3 others. Conclusions: Classification using morphological findings and TNRs on DbPET are useful to detect breast cancer. The DbPET classification should be considered for breast cancer screening. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.
1431P
Prediction of benign and malignant breast masses using digital mammograms texture features
C. Yanhua1, Y. Li2, J. Zhu3, J. Dong1 School of Information Science and Engineering, University of Jinan, Jinan, China, 2 Department of Radiology, Shandong Tumor hospital affiliated to Shandong University, Jinan, China, 3Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital affiliated to Shandong University, Jinan, China
1
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. 10fold 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. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.
doi:10.1093/annonc/mdz257 | v581
Downloaded from https://academic.oup.com/annonc/article-abstract/30/Supplement_5/mdz257.026/5576970 by guest on 14 October 2019
N. Li1, Y. Cheng2, L. Chen1, H. Zuo1, Y. Weng1, J. Zhou1, H. Liu1, M. Peng1, Q. Song1 1 Oncology, Renmin Hospital of Wuhan University, Wuhan, China, 2Gynaecology, Renmin Hospital of Wuhan University, Wuhan, China
1430P
Annals of Oncology Disclosure: P. Heudel: Research grant / Funding (institution): AstraZeneca; Honoraria (institution): novartis; Honoraria (self): Pfizer. O. Tredan: Honoraria (self): Roche; Honoraria (self): Pfizer; Honoraria (self): novartis; Honoraria (self): AstraZeneca; Honoraria (self): lilly; Honoraria (self): BMS; Honoraria (self): MSD. All other authors have declared no conflicts of interest.
1428P
Circulating tumour cell detection in epithelial ovarian cancer using dual-component antibodies targeting EpCAM and FRa
Background: Circulating tumor cell (CTC) detection methods based on epithelial cell adhesion molecule (EpCAM) have low detection rates in epithelial ovarian cancer (EOC). Meanwhile, folate receptor alpha (FRa) has high expression in EOC cells. We explored the feasibility of combining FRa and EpCAM as CTC capture targets in EOC. Methods: EpCAM and FRa antibodies were linked to magnetic nanospheres (MNs) using the principle of carbodiimide chemistry. Blood samples from healthy donor spiked with A2780 ovarian cancer cells were used for detecting the capture rate. Ninetyfive blood samples from 30 patients with EOC were used for comparing the positive rate of detection when using anti-EpCAM-MNs alone with that when using combination of anti-EpCAM-MNs and anti-FRa-MNs. Samples from 28 patients initially diagnosed with EOC and who did not undergo any treatment and 20 patients with ovarian benign disease were used for evaluating the sensitivity and specificity of combination of anti-EpCAM-MNs and anti-FRa-MNs. Results: Regression analysis between the number of recovered and that of spiked A2780 cells revealed yEpCAM ¼ 0.535x (R2 ¼ 0.99), yFRa ¼ 0.901x (R2 ¼ 0.99) and yEpCAMþFRa ¼ 0.928x (R2 ¼ 0.99). In mixtures of A2780 and MCF7 cells, the capture rate was 92% using the combination of anti-EpCAM-MNs and anti-FRa-MNs, exceeding the rate when using anti-EpCAM-MNs or anti-FRa-MNs alone by approximately 20% (P < 0.01). The combination of anti-EpCAM-MNs and anti-FRa-MNs showed significantly increased positive rate compared with anti-EpCAM-MNs alone (v2 ¼ 14.45, P < 0.001). Sensitivity values were 0.536 and 0.75 when using anti-EpCAM-MNs alone and when using the combination of anti-EpCAM-MNs and anti-FRa-MNs, respectively. Specificity values were 0.9 and 0.85, respectively. The combination of antiEpCAM-MNs and anti-FRa-MNs improved the sensitivity of CTC detection in patients with newly diagnosed EOC (v2 ¼ 4.17; P ¼ 0.041). Conclusions: The combination of FRa and EpCAM is feasible as a CTC capture target of CTC detection in patients with EOC. Legal entity responsible for the study: The authors. Funding: The NNSFC (National Natural Science Foundation of China) (81802980, 81770169, 81670144) and the Health Committee Research Project Fund of Hubei Province (WJ2019M179). Disclosure: All authors have declared no conflicts of interest.
1429P
CEUS of the breast: Is it feasible in improved performance of BI-RADS evaluation of critical breast lesions? A multi-center prospective study in China
J. Luo Ultrasound, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu, China Background: Ultrasound is the first and primary breast screening for thoes women with small and dense breast and is superior to mammography. BI-RADS using ultrasound causes approxinately over 40%-60% false-positive results, unnecessary biopsy and relatively low cancer-to biosy rate. This multi-center study in China is to determine whether contrastenhanced ultrasound (CEUS) of the breast can improve the precision of BI-RADS. Methods: 1721 patients were enrolled at 8 sites in China. CEUS was performed before core needle biopsy or surgical resection and a revised BI-RADS classification was assigned based on CEUS performance. Using pathological results as golden standerd to evaluate the diagnostic performance of CEUS-based BI-RADS. Results: 1738 solid breast lesions (5.0-39.8mm, 17.896 8.65mm) classified as BI-RADS 4 or 5 on conventional ultrasound or mammography. 771/1738(44.36%) are malignant and 967/1738(55.64%) are benign. The CEUS-based BI-RADS evaluation classified 402/1738 (23.13%) lesions into category 3 and its accuracy, sensitivity, specificity, positive and negative predictive values of 65.0%, 97.0%, 40.0%, 56.0% and 94.0%. The cancer-to-biopsy yield was 57.71% with CEUS-based BI-RADS 3 selected as the biopsy threshold compared with 44.36% otherwise, while the total biopsy rate was only 76.87% compared with 100% otherwiseand will reduce 39.5% (382/967) unnecessary biopsy rate in those benign nodules. Overall, only 2.59% of invasive cancers were misdiagnosed similar as BI-RADS 3 we use nowadays. Conclusions: This study suggests that evaluation of BI-RADS 4 or 5 breast lesions with CEUS result in reduced biopsy rates and increased cancer-to-biopsy yields. Legal entity responsible for the study: Jun Luo. Funding: Has not received any funding. Disclosure: The author has declared no conflicts of interest.
Volume 30 | Supplement 5 | October 2019
Classification of abnormal findings on ring-type dedicated breast PET for detecting breast cancer
S. Sasada, N. Masumoto, M. Nishina, Y. Kimura, A. Amioka, T. Itagaki, A. Emi, T. Kadoya, M. Okada Surgical Oncology, Institute of Biomedical & Health Sciences, Hiroshima University, Hiroshima, Japan Background: Ring-type dedicated breast positron emission tomography (DbPET) can detect small breast cancers; however, there are no category classifications of abnormal findings on DbPET such as BI-RADs (mammography, ultrasonography, and magnetic resonance imaging). We investigated whether the classification of DbPET findings was useful for detecting breast cancer. Methods: A total of 674 patients with breast cancers underwent ring-type DbPET using FDG before treatment between January 2016 and March 2019. Findings were morphologically categorized as a focus (uptake size 5 mm), mass (>6 mm), or non-mass (multiple uptakes). Non-mass uptakes were additionally classified based on the distribution: focal, linear, regional, segmental, and diffuse. Maximum standardized uptake value (SUVmax) and tumor-to-normal tissue ratio (TNR) were calculated. The final diagnosis was pathologically evaluated based on biopsy or surgical specimens, and lesions of category 2 or lower by conventional examinations were determined benign. Results: Among 867 abnormal findings, 668 (77%) were malignant and 199 (23%) were benign. Morphologically, 187 (21.6%) lesions were foci, 413 (47.6%) were masses, and 267 (30.8%) were non-masses. Among non-mass lesions, 131 focal, 1 linear, 15 regional, 115 segmental, and 5 diffuse distributions were presented. The median SUVmax was 5.0 and TNR was 2.8. The area under the curve values of SUVmax and TNR for predicting malignancy were 0.824 and 0.855, respectively. In a multivariate analysis, mass, focal and segmental distributions of non-mass lesions, high TNR were significantly related with breast cancer (all P < 0.001). Pathologically confirmed benign lesions included 45 mastopathies, 29 papillomas, 10 fibroadenomas, 7 ductal adenomas, and 3 others. Conclusions: Classification using morphological findings and TNRs on DbPET are useful to detect breast cancer. The DbPET classification should be considered for breast cancer screening. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.
1431P
Prediction of benign and malignant breast masses using digital mammograms texture features
C. Yanhua1, Y. Li2, J. Zhu3, J. Dong1 School of Information Science and Engineering, University of Jinan, Jinan, China, 2 Department of Radiology, Shandong Tumor hospital affiliated to Shandong University, Jinan, China, 3Department of Radiation Oncology Physics and Technology, Shandong Cancer Hospital affiliated to Shandong University, Jinan, China
1
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. 10fold 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. Legal entity responsible for the study: The authors. Funding: Has not received any funding. Disclosure: All authors have declared no conflicts of interest.
doi:10.1093/annonc/mdz257 | v581
Downloaded from https://academic.oup.com/annonc/article-abstract/30/Supplement_5/mdz257.026/5576970 by guest on 14 October 2019
N. Li1, Y. Cheng2, L. Chen1, H. Zuo1, Y. Weng1, J. Zhou1, H. Liu1, M. Peng1, Q. Song1 1 Oncology, Renmin Hospital of Wuhan University, Wuhan, China, 2Gynaecology, Renmin Hospital of Wuhan University, Wuhan, China
1430P