Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions

Acevedo, Francisco; Armengol, Victor Diego; Deng, Zhengyi; Tang, Rong; Coopey, Suzanne B.; Braun, Danielle; Yala, Adam; Barzilay, Regina; Li, Clara; Colwell, Amy S.; Guidi, Anthony J.; Cetrulo, Curtis L.; Garber, Judy E.; Smith, Barbara L.; King, Tari A.; Hughes, Kevin S.

doi:10.1007/s10549-018-4962-0

Cited by 25 publications

(22 citation statements)

References 37 publications

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“…The cancer rate in patients with no history of breast cancer in previous studies ranged from 0 to 1% 12–26 . Similar to those studies, the cancer rate in our study was 0.36%.…”

Section: Discussionsupporting

confidence: 89%

“…In our study, the rate of AMBL in patients with no history of breast cancer was 3.3%. The previous studies that included only patients with no history of breast cancer reported the rate of AMBL to be between 1.5 and 14% 5,7–26 . The wide range of reported rates of AMBL in different studies can be attributed to different pathological assessment methods of RM specimens between institutions.…”

Section: Discussionmentioning

confidence: 98%

“…In this context, previous studies that analysed RM specimens showed a higher rate of incidental breast cancer in patients with a history of breast cancer compared to patients with no history of breast cancer 5–11 . The rate of incidental atypical and malignant breast lesions (AMBL) in patients with no history of breast cancer ranged from 1.5 to 14% 5,7–26 . One of the most common findings in all these studies was the older age of patients with AMBL compared to patients with no AMBL.…”

Section: Introductionmentioning

confidence: 89%

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The rate of incidental atypical and malignant breast lesions in reduction mammoplasty specimens

et al. 2020

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Aims: Reduction mammoplasty (RM) is one of the most common plastic surgeries in the United States. We aimed to demonstrate the rate of incidental atypical and malignant breast lesions (AMBL) found in RM specimens and the impact of the number of submitted tissue sections on the rate of AMBL. Methods and results: We analysed our database for patients who had undergone reduction mammoplasty between 2000 and 2018. Patients with a history of breast cancer were excluded from the study. All pathology reports were analysed for AMBL (ALH, LCIS, FEA, ADH, DCIS, invasive carcinoma). The grossing protocol was to submit 10 sections from each breast between 2000 and 2013 and six sections between 2014 and 2018. One hundred and sixtynine of 5208 patients (3.3%) and 216 of 10 340 RM specimens (2.1%) showed at least one AMBL. Nineteen (0.36%) patients had incidental cancer. The median age of patients with AMBL was significantly higher than patients without ABL (aged 59 years versus 45 years). There was no cancer in patients aged <30 years. The age-controlled rate of overall AMBL as well as atypia and cancer only did not decrease by submitting fewer sections during the 2014-18 period compared to the 2010-13 period. Conclusions: Decreasing the number of tissue sections from 10 to six did not lead to a significant decrease in the rate of overall AMBL or cancer. Our data suggest that submitting six tissue sections from each breast for patients aged >30 years and two sections from each breast for patients aged <30 years would be sufficient.

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“…The cancer rate in patients with no history of breast cancer in previous studies ranged from 0 to 1% 12–26 . Similar to those studies, the cancer rate in our study was 0.36%.…”

Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 89%

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The rate of incidental atypical and malignant breast lesions in reduction mammoplasty specimens

et al. 2020

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“…We demonstrated that NLP models can achieve high performance in parsing both English and Chinese pathology reports . We used this system to identify sets of specific characteristics that helped us to quantify the risk and efficacy of chemoprevention in women with atypical breast lesions and to identify atypical breast lesions and cancers among patients who underwent mammoplasty . Others have applied NLP techniques to parse breast image reports .…”

Section: Implementations Of Nlp In Clinical Genetics and Breast Cancementioning

confidence: 99%

Natural language processing to facilitate breast cancer research and management

et al. 2019

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The medical literature has been growing exponentially, and its size has become a barrier for physicians to locate and extract clinically useful information. As a promising solution, natural language processing (NLP), especially machine learning (ML)-based NLP is a technology that potentially provides a promising solution. ML-based NLP is based on training a computational algorithm with a large number of annotated examples to allow the computer to "learn" and "predict" the meaning of human language.Although NLP has been widely applied in industry and business, most physicians still are not aware of the huge potential of this technology in medicine, and the implementation of NLP in breast cancer research and management is fairly limited. With a real-world successful project of identifying penetrance papers for breast and other cancer susceptibility genes, this review illustrates how to train and evaluate an NLPbased medical abstract classifier, incorporate it into a semiautomatic meta-analysis procedure, and validate the effectiveness of this procedure. Other implementations of NLP technology in breast cancer research, such as parsing pathology reports and mining electronic healthcare records, are also discussed. We hope this review will help breast cancer physicians and researchers to recognize, understand, and apply this technology to meet their own clinical or research needs. K E Y W O R D Sbreast cancer, genetics, medical literature, natural language processing

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“…Frame Elements References CANCER DIAGNOSIS NAME: cancer type [44], [45], [42], [46], [47], [48], [49], [50], [51] ANATOMICAL SITE: the location description of the finding (including primary and metastatic sites) [45], [52], [42], [53], [54], [55], [25], [27], [56], [57] HISTOLOGY: histological description (e.g. carcinoma) [44], [52], [58], [55], [53], [54], [4], [27], [59], [43], [57] GRADE: appearance of the cancerous cells, can be frame with further information (GRADING VALUE) [44], [52], [54], [4], [48], [27], [59], [60], [43], [61], [62] INVASION TYPE: the stage or level of invasion [52] TUMOR BLOCK: tissue cores removed from regions of interest in paraffinembedded tissues (e.g.…”

Section: Framementioning

confidence: 99%

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

Datta

Bernstam

Roberts

2019

Journal of Biomedical Informatics

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Objective:There is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer. Methods:We identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 79 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps.Results: Frames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 79), with recent work focusing on extracting information related to treatment and breast cancer diagnosis. Conclusion:The list of common frames described in this paper identifies important cancerrelated information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.

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Pathologic findings in reduction mammoplasty specimens: a surrogate for the population prevalence of breast cancer and high-risk lesions

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Cited by 25 publications

References 37 publications

The rate of incidental atypical and malignant breast lesions in reduction mammoplasty specimens

The rate of incidental atypical and malignant breast lesions in reduction mammoplasty specimens

Natural language processing to facilitate breast cancer research and management

A frame semantic overview of NLP-based information extraction for cancer-related EHR notes

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