Do Clinical Features and Survival of Single Hormone Receptor Positive Breast Cancers Differ from Double Hormone Receptor Positive Breast Cancers?

Ng, Char-Hong; Bhoo-Pathy, Nirmala; Taib, Nur Aishah Mohd; Ho, Gwo Fuang; Mun, Kein Seong; Rhodes, Anthony; Looi, Lai‐Meng; Yip, Cheng‐Har

doi:10.7314/apjcp.2014.15.18.7959

Cited by 17 publications

(10 citation statements)

References 21 publications

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“…Ng et al reported that the ER−/PgR+ phenotype had no distinct histopathological characteristics compared to the ER+/PgR+ and ER−/PgR− phenotypes and no prognostic impact [7]. In contrast, Rakha et al reported poorer survival of ER−/PgR+ cases compared with ER+/PgR+ cases [5].…”

Section: Discussionmentioning

confidence: 99%

Oestrogen receptor-negative/progesterone receptor-positive phenotype of invasive breast carcinoma in Japan: re-evaluated using immunohistochemical staining

et al. 2018

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Section: Discussionmentioning

confidence: 99%

Oestrogen receptor-negative/progesterone receptor-positive phenotype of invasive breast carcinoma in Japan: re-evaluated using immunohistochemical staining

et al. 2018

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“…Next, the data was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry (IHC). Receptor status was selected to cluster the dataset, mainly because it was used to classify the records obtained from breast cancer patients for further analysis in studies associated to survival prediction [8, 9, 37, 38]. Three clusters were segregated based on Estrogen receptor (ER) status, Progesterone receptor (PR) status, and c-er-b2 status, as listed in Table 3.…”

Section: Methodsmentioning

confidence: 99%

“…Previously clinicians have used basic software programs, such as Microsoft Excel, SPSS, and STATA [7–9], in analysing factors influencing breast cancer survival rate. These conventional statistical methods are not really adaptable in identifying new variables as well as generating creative and integrative visualisations [10].…”

Section: Introductionmentioning

confidence: 99%

Predicting factors for survival of breast cancer patients using machine learning techniques

Ganggayah

Taib

Har

et al. 2019

BMC Med Inform Decis Mak

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216

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BackgroundBreast cancer is one of the most common diseases in women worldwide. Many studies have been conducted to predict the survival indicators, however most of these analyses were predominantly performed using basic statistical methods. As an alternative, this study used machine learning techniques to build models for detecting and visualising significant prognostic indicators of breast cancer survival rate.MethodsA large hospital-based breast cancer dataset retrieved from the University Malaya Medical Centre, Kuala Lumpur, Malaysia (n = 8066) with diagnosis information between 1993 and 2016 was used in this study. The dataset contained 23 predictor variables and one dependent variable, which referred to the survival status of the patients (alive or dead). In determining the significant prognostic factors of breast cancer survival rate, prediction models were built using decision tree, random forest, neural networks, extreme boost, logistic regression, and support vector machine. Next, the dataset was clustered based on the receptor status of breast cancer patients identified via immunohistochemistry to perform advanced modelling using random forest. Subsequently, the important variables were ranked via variable selection methods in random forest. Finally, decision trees were built and validation was performed using survival analysis.ResultsIn terms of both model accuracy and calibration measure, all algorithms produced close outcomes, with the lowest obtained from decision tree (accuracy = 79.8%) and the highest from random forest (accuracy = 82.7%). The important variables identified in this study were cancer stage classification, tumour size, number of total axillary lymph nodes removed, number of positive lymph nodes, types of primary treatment, and methods of diagnosis.ConclusionInterestingly the various machine learning algorithms used in this study yielded close accuracy hence these methods could be used as alternative predictive tools in the breast cancer survival studies, particularly in the Asian region. The important prognostic factors influencing survival rate of breast cancer identified in this study, which were validated by survival curves, are useful and could be translated into decision support tools in the medical domain.

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“…Additionally, ER (+) BC patients have longer relapse-free survival and overall survival compared to the ER (-) breast carcinomas (Hoch et al, 1999;Zhang et al, 2014). As part of BC treatment, the presence of ER has become an important determinant for predicting the response to adjuvant endocrine therapy with tamoxifen (Dowsett et al, 2008;Ng et al, 2014). However, 30-50% of these cases do not respond to or develop resistance to tamoxifen (Girault et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Association of miR-1266 with Recurrence/Metastasis Potential in Estrogen Receptor Positive Breast Cancer Patients

Sevinc

Egelí²,

Çeçener³

et al. 2015

Asian Pacific Journal of Cancer Prevention

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The Homeobox B13 (HOXB13):Interleukin 17 Receptor B (IL17BR) index of estrogen receptor (ER)-positive breast cancer (ER (+) BC) patients may be a potential biomarker of recurrence/ metastasis. However, effects of microRNA (miRNA) binding to the 3' untranslated region (3´UTR) of HOXB13 and IL17BR and its function on recurrence/metastasis in ER (+) BC remains elusive. The aims of this study were to determine the expression of miRNAs that bind to 3´UTR of HOXB13 and IL17BR in ER (+) BC patients and asess the effects of these miRNAs on recurrence/metastasis. The expression profiles of HOXB13 and IL17BR were evaluated using RT-PCR in tumors and normal tissue samples from 40 ER (+) BC patients. The expression level of 4 miRNAs, which were predicted to bind the 3´UTR of HOXB13 and IL17BR using TargetScan, microRNA.org and miRDB online databases, were further evaluated with RT-PCR. Our findings demonstrated that high miR-1266 levels might be significant prognostic factor for recurrence/metastasis occurrence (3.05 fold p=0.004) and tamoxifen response (3.90 fold; p=0.2514) in ER (+) BC cases. Although we suggest that modulation of miR-1266 expression may be an important mechanism underlying the chemoresistance of ER (+) BC, advanced studies and validation are required.

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Do Clinical Features and Survival of Single Hormone Receptor Positive Breast Cancers Differ from Double Hormone Receptor Positive Breast Cancers?

Cited by 17 publications

References 21 publications

Oestrogen receptor-negative/progesterone receptor-positive phenotype of invasive breast carcinoma in Japan: re-evaluated using immunohistochemical staining

Oestrogen receptor-negative/progesterone receptor-positive phenotype of invasive breast carcinoma in Japan: re-evaluated using immunohistochemical staining

Predicting factors for survival of breast cancer patients using machine learning techniques

Association of miR-1266 with Recurrence/Metastasis Potential in Estrogen Receptor Positive Breast Cancer Patients

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