Dataset Information

Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.

ABSTRACT: Information about the expression status of hormone receptors such as estrogen receptor (ER), progesterone receptor (PR), and Her-2 is crucial in the management and prognosis of breast cancer. Therefore, the retrieval and analysis of hormone receptor expression characteristics in metastatic breast cancer may be valuable in breast cancer study. Herein, we report a text mining tool based on word/phrase matching that retrieves hormone receptor expression data of regional or distant metastatic breast cancer from pathology reports. It was tested on pathology reports at the China Medical University Hospital from 2013 to 2018. The tool showed specificities of 91.6% and 63.3% for the detection of regional lymph node metastasis and distant metastasis, respectively. Sensitivity in immunohistochemical study result extraction in these cases was 98.6% for distant metastasis and 78.3% for regional lymph node metastasis. Statistical analysis on these retrieved data showed significant difference s in PR and Her-2 expressions between regional and metastatic breast cancer, which is compatible with previous studies. In conclusion, our study shows that metastatic breast cancer hormone receptor expression characteristics can be retrieved by text mining. The algorithm designed in this study may be useful in future studies about text mining in pathology reports.

SUBMITTER: Chang KP

PROVIDER: S-EPMC7273481 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.

Chang Kai-Po KP Wang John J Chang Chi-Chang CC Chu Yen-Wei YW

BioMed research international 20200523

Information about the expression status of hormone receptors such as estrogen receptor (ER), progesterone receptor (PR), and Her-2 is crucial in the management and prognosis of breast cancer. Therefore, the retrieval and analysis of hormone receptor expression characteristics in metastatic breast cancer may be valuable in breast cancer study. Herein, we report a text mining tool based on word/phrase matching that retrieves hormone receptor expression data of regional or distant metastatic breast ...[more]

PMID: 32566676

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Project description:Randomized clinical trials constitute the gold-standard for evaluating new anti-cancer therapies; however, real-life data are key in complementing clinically useful information. We developed a computational tool for real-life data analysis and applied it to the metastatic colorectal cancer (mCRC) setting. This tool addressed the impact of oncology/non-oncology parameters on treatment patterns and clinical outcomes.The developed tool enables extraction of any computerized information including comorbidities and use of drugs (oncological/non-oncological) per individual HMO member. The study in which we evaluated this tool was a retrospective cohort study that included Maccabi Healthcare Services members with mCRC receiving bevacizumab with fluoropyrimidines (FP), FP plus oxaliplatin (FP-O), or FP plus irinotecan (FP-I) in the first-line between 9/2006 and 12/2013.The analysis included 753 patients of whom 15.4% underwent subsequent metastasectomy (the Surgery group). For the entire cohort, median overall survival (OS) was 20.5 months; in the Surgery group, median duration of bevacizumab-containing therapy (DOT) pre-surgery was 6.1 months; median OS was not reached. In the Non-surgery group, median OS and DOT were 18.7 and 11.4 months, respectively; no significant OS differences were noted between FP-O and FP-I, whereas FP use was associated with shorter OS (12.3 month; p <0.002; notably, these patients were older). Patients who received both FP-O- and FP-I-based regimens achieved numerically longer OS vs. those who received only one of these regimens (22.1 [19.9-24.0] vs. 18.9 [15.5-21.9] months). Among patients assessed for wild-type KRAS and treated with subsequent anti-EGFR agent, OS was 25.4 months and 18.7 months for 124 treated vs. 37 non-treated patients (non-significant). Cox analysis (controlling for age and gender) identified several non-oncology parameters associated with poorer clinical outcomes including concurrent use of diuretics and proton-pump inhibitors.Our tool provided insights that confirmed/complemented information gained from randomized-clinical trials. Prospective tool implementation is warranted.

Project description:Background:Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenemia, oligo-anovulation, and numerous ovarian cysts. Hospital electronic medical records provide an avenue for investigating polycystic ovary morphology commonly seen in PCOS at a large scale. The purpose of this study was to develop and evaluate the performance of two machine learning text algorithms, for classification of polycystic ovary morphology (PCOM) in pelvic ultrasounds. Methods:Pelvic ultrasound reports from patients at Boston Medical Center between October 1, 2003 and December 12, 2016 were included for analysis, which resulted in 39,093 ultrasound reports from 25,535 unique women. Following the 2003 Rotterdam Consensus Criteria for polycystic ovary syndrome, 2000 randomly selected ultrasounds were expert labeled for PCOM status as present, absent, or unidentifiable (not able to be determined from text alone). An ovary was marked as having PCOM if there was mention of numerous peripheral follicles or if the volume was greater than 10?ml in the absence of a dominant follicle or other confounding pathology. Half of the labeled data was used to develop and refine the algorithms, and the other half was used as a test set for evaluating its accuracy. Results:On the evaluation set of 1000 random US reports, the accuracy of the classifiers were 97.6% (95% CI: 96.5, 98.5%) and 96.1% (94.7, 97.2%). Both models were more adept at identifying PCOM-absent ultrasounds than either PCOM-unidentifiable or PCOM-present ultrasounds. The two classifiers estimated prevalence of PCOM within the whole set of 39,093 ultrasounds to be 44% PCOM-absent, 32% PCOM-unidentifiable, and 24% PCOM-present. Conclusions:Although accuracy measured on the test set and inter-rater agreement between the two classifiers (Cohen's Kappa?=?0.988) was high, a major limitation of our approach is that it uses the ultrasound report text as a proxy and does not directly count follicles from the ultrasound images themselves.

Project description:ObjectiveDespite the promising efficacy of the novel antibody-drug conjugate trastuzumab deruxtecan in treating Hormone Receptor (HoR)-positive/Human Epidermal Growth Factor Receptor 2 (HER2)-low metastatic breast cancer (MBC), its categorization as a distinct entity remains disputed, as does the divergence in its endocrine and chemotherapy outcomes. This study aimed to elucidate the clinical characteristics, primary/metastatic lesion HER2 expression, and treatment outcomes of HoR-positive/HER2-low patients.MethodsWe included HoR-positive/HER2-negative MBC patients who underwent 1st and 2nd line endocrine treatment from July 2010 to October 2022 at the Fudan University Shanghai Cancer Center, comparing the clinical pathological characteristics, HER2 expression in primary/metastatic lesions, treatment, and therapeutic effects of the HER2-low and HER2-zero groups.ResultsAmong the 458 HoR-positive/HER2-negative MBC patients, 54.37% (249/458) were HER2-low. The HER2-low group and the HER2-zero group had similar clinical pathological characteristics and similar progression-free survival (PFS) of 1st and 2nd line endocrine treatment (median PFS: 8.05 months vs 10.12 months, p=0.114, HR 1.257, 95% CI 0.771 to 1.028). The PFS of the HER2-low and HER2-zero groups was also similar, treated with different endocrine drugs (including aromatase inhibitors, tamoxifen/toremifene, fulvestrant, palbociclib, and everolimus). However, the HER2-low group had significantly shorter PFS during 1st and 2nd line chemotherapy compared to the HER2-zero group (median PFS: 8.64 vs 9.03 months, p=0.027, HR 0.841, 95% CI 0.721-0.980). Additionally, 41.18% (63/153) of patients exhibited a change in HER2 expression between primary and metastatic lesions. Notably, patients whose HER2 status changed from zero to low expression had significantly prolonged PFS during chemotherapy compared to those who maintained low HER2 expression (median PFS: 14.29 vs 11.27 months, p=0.048, HR 0.597, 95% CI 0.358-0.996).ConclusionIn HoR-positive MBC, patients with low and zero HER2 expression have similar clinical characteristics and respond similarly to endocrine treatment, but the chemotherapy effect is worse in the HER2-low patients. Moreover, the transformation of HER2 status from primary to metastatic lesions may have potential influence on chemotherapy outcomes. Therefore, the expression and heterogeneity of HER2 should be considered in clinical decisions.

Project description:BackgroundElectronic medical records, including pathology reports, are often used for research purposes. Currently, there are few programs freely available to remove identifiers while leaving the remainder of the pathology report text intact. Our goal was to produce an open source, Health Insurance Portability and Accountability Act (HIPAA) compliant, deidentification tool tailored for pathology reports. We designed a three-step process for removing potential identifiers. The first step is to look for identifiers known to be associated with the patient, such as name, medical record number, pathology accession number, etc. Next, a series of pattern matches look for predictable patterns likely to represent identifying data; such as dates, accession numbers and addresses as well as patient, institution and physician names. Finally, individual words are compared with a database of proper names and geographic locations. Pathology reports from three institutions were used to design and test the algorithms. The software was improved iteratively on training sets until it exhibited good performance. 1800 new pathology reports were then processed. Each report was reviewed manually before and after deidentification to catalog all identifiers and note those that were not removed.Results1254 (69.7 %) of 1800 pathology reports contained identifiers in the body of the report. 3439 (98.3%) of 3499 unique identifiers in the test set were removed. Only 19 HIPAA-specified identifiers (mainly consult accession numbers and misspelled names) were missed. Of 41 non-HIPAA identifiers missed, the majority were partial institutional addresses and ages. Outside consultation case reports typically contain numerous identifiers and were the most challenging to deidentify comprehensively. There was variation in performance among reports from the three institutions, highlighting the need for site-specific customization, which is easily accomplished with our tool.ConclusionWe have demonstrated that it is possible to create an open-source deidentification program which performs well on free-text pathology reports.

Dataset Information

Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.

Publications

Development of a Novel Tool for the Retrieval and Analysis of Hormone Receptor Expression Characteristics in Metastatic Breast Cancer via Data Mining on Pathology Reports.

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