Abstract PR-04: Effect of breast cancer chemoprevention on a convolutional neural network-based mammographic evaluation using a mammographic dataset of women with atypical hyperplasia, lobular or ductal carcinoma in situ

Author(s):  
Julia E. McGuinness ◽  
Vicky Ro ◽  
Aishwarya Anuraj ◽  
Haley Manley ◽  
Simukayi Mutasa ◽  
...  
2015 ◽  
Vol 21 (4) ◽  
pp. 377-386 ◽  
Author(s):  
Laura L. Reimers ◽  
Parijatham S. Sivasubramanian ◽  
Dawn Hershman ◽  
Mary Beth Terry ◽  
Heather Greenlee ◽  
...  

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 1561-1561
Author(s):  
Francisco Acevedo ◽  
Victor Diego Armengol ◽  
Zhengyi Deng ◽  
Rong Tang ◽  
Suzanne Coopey ◽  
...  

1561 Background: Proliferative breast lesions with atypia (atypical hyperplasia and lobular carcinoma in-situ (LCIS)) increase the risk of breast cancer (BC). Most cases are diagnosed in the context of an abnormal mammogram. Little is known about BC risk for patients with these lesions who are asymptomatic. Mammoplasty specimens allow us to study breast tissue in asymptomatic healthy women. We previously published the rate of atypia in the largest reported mammoplasty cohort. The aim of this study is to examine the risk of BC in the atypia cohort. Methods: Breast pathology reports were retrospectively reviewed for evidence of atypical ductal hyperplasia (ADH), atypical lobular hyperplasia (ALH) or LCIS in bilateral reduction mammoplasty specimens from five institutions within a single healthcare system between 1990 to 2017. Patients with prior or concurrent BC or prior atypia were excluded. Data was extracted from electronic medical records using natural language processing and manual review to assess subsequent risk of BC. Results: From our mammoplasty cohort of 4771 patients, 295 patients were found to have atypia (6.2%) at baseline. 40 of these patients were lost to follow-up and excluded from the study. For the remaining 255 patients, 13 had severe ADH bordering on ductal carcinoma in situ, 52 had LCIS, 119 had ALH, and 71 had ADH at baseline. The median age at baseline was 52.1 (range 17.9 – 74.3). With a median follow-up of 7.7 years, of the 255 patients 9 patients developed BC (8 invasive carcinomas, 1 ductal carcinoma in situ). 81.3% of the cohort did not receive chemoprevention. Only one patient out of the nine who developed BC received chemoprevention. The risk of developing BC among women with atypia at baseline was 0.5%, 2.9% and 4.1%, at 3, 5 and 10 years respectively. Conclusions: Patients with asymptomatic atypias found in reduction mammoplasty specimens appear to be at lower risk of developing BC than those diagnosed with atypia in the context of an abnormal mammogram. These results may provide guidance on how to manage this group of patients related to future screening and/or chemoprevention.


Author(s):  
Yu-Dong Zhang ◽  
Suresh Chandra Satapathy ◽  
Di Wu ◽  
David S. Guttery ◽  
Juan Manuel Górriz ◽  
...  

AbstractDuctal carcinoma in situ (DCIS) is a pre-cancerous lesion in the ducts of the breast, and early diagnosis is crucial for optimal therapeutic intervention. Thermography imaging is a non-invasive imaging tool that can be utilized for detection of DCIS and although it has high accuracy (~ 88%), it is sensitivity can still be improved. Hence, we aimed to develop an automated artificial intelligence-based system for improved detection of DCIS in thermographs. This study proposed a novel artificial intelligence based system based on convolutional neural network (CNN) termed CNN-BDER on a multisource dataset containing 240 DCIS images and 240 healthy breast images. Based on CNN, batch normalization, dropout, exponential linear unit and rank-based weighted pooling were integrated, along with L-way data augmentation. Ten runs of tenfold cross validation were chosen to report the unbiased performances. Our proposed method achieved a sensitivity of 94.08 ± 1.22%, a specificity of 93.58 ± 1.49 and an accuracy of 93.83 ± 0.96. The proposed method gives superior performance than eight state-of-the-art approaches and manual diagnosis. The trained model could serve as a visual question answering system and improve diagnostic accuracy.


2018 ◽  
Vol 23 (4) ◽  
pp. 237-248 ◽  
Author(s):  
Hugo Villanueva ◽  
Sandra Grimm ◽  
Sagar Dhamne ◽  
Kimal Rajapakshe ◽  
Adriana Visbal ◽  
...  

Abstract Ductal carcinoma in situ (DCIS) is a non-obligate precursor to most types of invasive breast cancer (IBC). Although it is estimated only one third of untreated patients with DCIS will progress to IBC, standard of care for treatment is surgery and radiation. This therapeutic approach combined with a lack of reliable biomarker panels to predict DCIS progression is a major clinical problem. DCIS shares the same molecular subtypes as IBC including estrogen receptor (ER) and progesterone receptor (PR) positive luminal subtypes, which encompass the majority (60–70%) of DCIS. Compared to the established roles of ER and PR in luminal IBC, much less is known about the roles and mechanism of action of estrogen (E2) and progesterone (P4) and their cognate receptors in the development and progression of DCIS. This is an underexplored area of research due in part to a paucity of suitable experimental models of ER+/PR + DCIS. This review summarizes information from clinical and observational studies on steroid hormones as breast cancer risk factors and ER and PR as biomarkers in DCIS. Lastly, we discuss emerging experimental models of ER+/PR+ DCIS.


PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0160835
Author(s):  
Hoe Suk Kim ◽  
Minji Jung ◽  
Sul Ki Choi ◽  
Woo Kyung Moon ◽  
Seung Ja Kim

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