Diagnostic Value of Ultrasound-guided Fine Needle Aspiration Cytology by a Endocrine Surgeon

Oh, Bu Hee; Park, Young Sam; Sung, Chi Won; Kim, Cheol Seung

doi:10.16956/kjes.2008.8.3.189

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…However, there is a difficulty in accurate diagnosis since fine-cell aspiration is affected by various external variables, such as the small size of cancer, noninvasive breast cancer, and low stage [7]. In addition, diagnosis can be inaccurate due to various factors, such as the skills of the examiner and the similarity between organisms [89]. To overcome these limitations, it is necessary to provide an auxiliary tool to diagnose breast cancer accurately.…”

Section: Introductionmentioning

confidence: 99%

Stacking Ensemble Technique for Classifying Breast Cancer

2019

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ObjectivesBreast cancer is the second most common cancer among Korean women. Because breast cancer is strongly associated with negative emotional and physical changes, early detection and treatment of breast cancer are very important. As a supporting tool for classifying breast cancer, we tried to identify the best meta-learner model in a stacking ensemble when the same machine learning models for the base learner and meta-learner are used.MethodsWe used machine learning models, such as the gradient boosted model, distributed random forest, generalized linear model, and deep neural network in a stacking ensemble. These models were used to construct a base learner, and each of them was used as a meta-learner again. Then, we compared the performance of machine learning models in the meta-learner to determine the best meta-learner model in the stacking ensemble.ResultsExperimental results showed that using the GBM as a meta-learner led to higher accuracy than that achieved with any other model for breast cancer data and using the GLM as a meta learner led to low root-mean-squared error for both sets of breast cancer data.ConclusionsWe compared the performance of every meta-learner model in a stacking ensemble as a supporting tool for classifying breast cancer. The study showed that using specific models as a metalearner resulted in better performance than single classifiers, and using GBM and GLM as a meta-learner is appropriate as a supporting tool for classifying breast cancer data.

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Section: Introductionmentioning

confidence: 99%

Stacking Ensemble Technique for Classifying Breast Cancer

2019

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Optimized Ensemble Prediction Model for Breast Cancer

Aditya

2021

2021 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE)

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The Correlation between the Fine Needle Aspiration Cytology and Histology of Patients Who Have Undergone Thyroidectomy

Baek

Kim

Bae

et al. 2009

Korean J Endocr Surg

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Purpose: Fine needle aspiration cytology (FNAC) has become a standard diagnostic tool for thyroid nodules. The purpose of this study is to analyze the correlation between FNAC and the final histology to improve the diagnostic value. Methods: We collected 792 patients who had undergone thyroidectomy, and we selected 549 patients who had undergone preoperative FNAC. The FNAC results were classified as non-diagnostic, malignancy, indeterminate and benign. The final pathologic reports were classified into two categories: malignant or benign. The individual histology was also identified. We retrospectively analyzed the pathologic result, the sensitivity, the specificity and the predictive value of the FNAC. Results: Among 549 patients, their FNACs showed 47 non diagnostic results, 189 malignancies,136 indeterminate results and 177 benign results. The false positive rate was 1.1% and the false negative rate was 12.9%. The final pathology reports were 262 malignancies and 287 benign lesions. There were 42 (30.9%) malignancies among the 136 indeterminate cases. Adenomatous hyperplasia was the most common benign lesion and papillary carcinoma (containing micropapillary carcinoma) was the most common benign lesion. Conclusion: FNAC is an easy and safe diagnostic tool, but it still has many limitations. It is necessary to reduce the number of non diagnostic results. Technical advances, classification of the advanced categories of cytology and other staining methods can provide more specific results, and this can reduce the rate of performing unnecessary surgery.

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Diagnostic Value of Ultrasound-guided Fine Needle Aspiration Cytology by a Endocrine Surgeon

Cited by 3 publications

References 9 publications

Stacking Ensemble Technique for Classifying Breast Cancer

Stacking Ensemble Technique for Classifying Breast Cancer

Optimized Ensemble Prediction Model for Breast Cancer

The Correlation between the Fine Needle Aspiration Cytology and Histology of Patients Who Have Undergone Thyroidectomy

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