Cytological scoring of breast lesions and comparison with histopathological findings

Abraham, Betty; Sarojini, T R

doi:10.4103/joc.joc_84_17

Cited by 3 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[1] So it is necessary to distinguish benign from malignant conditions and identify the suspicious lesion so that in such cases biopsy can be done for confirmation and definitive treatment. [4] FNAC is included as a component of triple test which was introduced by Johansen C in 1975 for the diagnostic evaluation of breast lump in elderly women. This test avoids the unnecessary open biopsy or frozen section.…”

Section: Introductionmentioning

confidence: 99%

A Study of Fine Needle Aspiration Cytology of Breast Lump With The Use of National Health Service Breast Screening Program (NHSBSP) - Study Of 500 Cases

Shahu,

Gamit,

Sheth

et al. 2022

Ann of Pathol and Lab Med

View full text Add to dashboard Cite

Background: FNAC is rapid, simple, safe, cost effective and good screening procedure with high sensitivity and specificity. Clinically, the diseases of breast mainly present as palpable breast lump, pain and nipple discharge. Most of the breast lumps are benign but pose a significant anxiety to the patient, which can be allayed by giving assurance that, usually it is benign. So it is necessary to distinguish benign from malignant conditions Methods: The present study on was carried out during June 2017 to May 2019. We categorized the patients into C1 (Inadequate), C2 (Benign), C3 (Atypia, probably benign), C4 (Suspicious of malignancy) and C5 (Malignant) category according to NHSBSP and correlated histopathologically whenever available. Result: Total 500 cases examined. Majority of the cases were in the 3rd decade of the life. Benign category comprised of 368 cases (73.6%), followed by malignant disease 61 (12.2%). Fibroadenoma was most common comprising of 230 cases (62.3%) among benign cases. Out of 230 cases of Fibroadenoma, most (89) cases were in 21-30 years age group, followed by 11-20 years age group. Breast carcinoma was most common (31.1%) in 41-50 years age group and 61-70 (27.9%) years age group followed by 51-60 (19.7%) years age group. Conclusion: FNAC of breast lesions is advantageous for both clinicians and patients because of its immediate results, economy, and accuracy. FNAC has got significant diagnostic value in differentiating benign from malignant lesions providing us valuable information for planning of subsequent therapeutic management and avoiding unwanted surgeries.

show abstract

Section: Introductionmentioning

confidence: 99%

A Study of Fine Needle Aspiration Cytology of Breast Lump With The Use of National Health Service Breast Screening Program (NHSBSP) - Study Of 500 Cases

Shahu,

Gamit,

Sheth

et al. 2022

Ann of Pathol and Lab Med

View full text Add to dashboard Cite

show abstract

Calcium signal modulation in breast cancer aggressiveness

et al. 2023

View full text Add to dashboard Cite

Natural Language Processing Approaches for Automated Multilevel and Multiclass Classification of Breast Lesions on Free-Text Cytopathology Reports

Nandish

R.J.

N.M.

2022

JCO Clinical Cancer Informatics

View full text Add to dashboard Cite

PURPOSE The extensive growth and use of electronic health records (EHRs) and extending medical literature have led to huge opportunities to automate the extraction of relevant clinical information that helps in concise and effective clinical decision support. However, processing such information has traditionally been dependent on labor-intensive processes with human errors such as fatigue, oversight, and interobserver variability. Hence, this study aims at the processing of EHRs and performing multilevel and multiclass classification by fetching dominant characteristic features that are sufficient to detect and differentiate various types of breast lesions. PATIENTS AND METHODS In this study, unstructured EHRs on breast lesions obtained through fine-needle aspiration cytology technique are considered. The raw text was normalized into structured tabular form and converted to scores by performing sentiment analysis that helps to decide the total polarity or class label of the EHR. Supervised machine learning approaches, namely random forest and feed-forward neural network trained using Levenberg-Marquardt training function, are used for classification of the collected EHR data set containing 2,879 records that are split in the ratio of 80:20 as training and testing data sets, respectively. RESULTS Random forest and feed-forward neural network classifiers gave the best performance with an accuracy of 99.36%, an overall receiver operating characteristic-area under the curve of 99.2%, a correlation with ground truth of 98.3%, and a histopathologic correlation of 98.6%. CONCLUSION Natural language processing has huge potential to automate the extraction of clinical features from breast lesions. The proposed multilevel and multiclass classification approach is used to classify 13 different types of breast lesions with 20 different labels into five classes to decide the type of treatment that should be given to patients by a physician or oncologist.

show abstract

Cytological scoring of breast lesions and comparison with histopathological findings

Cited by 3 publications

References 18 publications

A Study of Fine Needle Aspiration Cytology of Breast Lump With The Use of National Health Service Breast Screening Program (NHSBSP) - Study Of 500 Cases

A Study of Fine Needle Aspiration Cytology of Breast Lump With The Use of National Health Service Breast Screening Program (NHSBSP) - Study Of 500 Cases

Calcium signal modulation in breast cancer aggressiveness

Natural Language Processing Approaches for Automated Multilevel and Multiclass Classification of Breast Lesions on Free-Text Cytopathology Reports

Contact Info

Product

Resources

About