Comparative Study of Classification Techniques on Breast Cancer FNA Biopsy Data

Accurate diagnostic detection of the cancerous cells in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Bayesian classifier and other Artificial neural network classifiers (Backpropagation, linear programming, Learning vector quantization, and K nearest neighborhood) on the Wisconsin breast cancer classification problem

Correlation of Fine Needle Aspiration Cytology and Histopathology Diagnosis in the Evaluation of Breast Lumps

Background: A large number of patients have been suffer from breast cancer worldwide and this trend is increasing. It is difficult to determine whether a lump is benign or malignant from clinical assessment; thus, the need for micropic and tissue analysis arises. Methods: This comparative retrospective cross sectional study was conducted in the Department of Pathology, Rehman Medical Institute, Peshawar, Pakistan from January 2006 to March 2013, to determine the value of fine needle aspiration cytology (FNAC) in the diagnosis of breast lump and to compare the result of FNAC with histological diagnosis to assess its accuracy. Results: Seventy-four  cases with breast lumps were presented for FNAC. Of these, 32.4% were reported as a C2 lesion, 4.1% were reported as benign with atypical cells (C3), 8.1% cases were suspicious for malignancy (C4), and 55.4% were positive for malignancy (C5). On histopathology examination, out of 24 cases which were reported as C2 lesions, 95.8% were benign and 4.1% turned out to be invasive ductal carcinoma. Of the cases that presented as C3 lesions, one was diagnosed as benign duct ectasia, one with ductal carcinoma in situ, and one with invasive ductal carcinoma on histopathological examination. The cases that were diagnosed as C4 lesions all turned out to be carcinoma on histopathology.  In this study, FNAC and histopathology diagnoses were strongly correlated [r 0.92, p <0.001]. Conclusion: Diagnosis of breast lump based on FNAC should be practiced as there is high correlation with histopathological finding. FNAC should be used as a routine..

Quantitative classification of breast fine needle aspirates using the AxioHOME system

Fine needle aspiration cytology is useful in the pre-operative assessment of patients with breast lumps. Lesions are reported as benign, suspicious or malignant. The number of suspicious categories is high in inexperienced hands thus limiting this useful diagnostic tool. The aim was to evaluate quantitative methods of classifying breast fine needle aspirates using the Highly Optimized Microscope Environment system. May- Grumwald Giemsa-stained archived slides were retrieved and smear quality assessed. Fifty epithelial cells corresponding to the cytological grading on each slide were measured using the system\'s general morphometry program. Generated data was exported to Microsoft Excel for analysis. A significant difference in the mean nuclear area, mean nuclear perimeter, mean largest nuclear diameter was found between the slides graded as benign, suspicious malignant and malignant. C2 & C4, D 1, P < 0.004. C4 & C5, Area P < 0.001. The means of the number count of nucleoli was able to distinguish between FNAC Benign and Malignant category C2/C5, P = 00440 and suspicious benign and malignant C3/C5 P = 0.00486. Morphometry could be useful in situations where experienced cytopathologists are unavailable especially when this program can also measure the degree of dispersion between cells Keywords: Morphometric image analysis, aspiration cytology, breast. International Journal of Biological and Chemical Sciences Vol. 2 (2) 2008: pp. 139-14

Utility Of Shear Wave Elastography In Breast Cancer Diagnosis: A Systematic Review And Meta-Analysis

In the United States, breast cancer is one of the most diagnosed cancers in women. Early detection, often via mammography, and intervention have been shown to reduce mortality. However, not all cancers are mammographically evident in early stages, if at all. As a result, ultrasound has been increasingly used to supplement mammography for breast cancer detection and assessment, particularly in dense breasts. Recent advancements in ultrasonography include the ability to characterize the stiffness of biological tissues. Shear Wave Elastography (SWE) is one such development used to quantify tissue stiffness within a region of interest. The resistance of soft tissue to deformation depends on the molecular makeup of the tissue components as well as elements of tissue structure, such as stromal and connective tissue. As tumor growth often involves architectural changes that cause increased stiffness compared to normal neighboring tissue, SWE has the potential to compliment mammography and B-mode ultrasound for breast lesion characterization. Studies establishing the clinical value of SWE may aid in its incorporation into diagnostic guidelines. This study aimed to quantify the performance of 2D SWE for differentiating benign and malignant breast lesions in women with abnormal mammography via a systematic review of the literature and meta-analysis. A systematic search of PubMed, Scopus, Embase, Ovid-Medline, Cochrane Library and Web of Science was performed. Studies of diagnostic accuracy published prior to June 2021 using SWE to evaluate abnormal breast tissue with at least 50 lesions that reported quantitative shear wave speed (SWS) parameters (the mean (SWSmean), maximum (SWSmax), minimum (SWSmin), or standard deviation (SWSSD) of the SWS) and thresholds and included a reference standard of either biopsy or 2-year stability were included in the analysis. The QUADAS- 2 tool was used to assess possible bias within studies as well as their applicability. 87 studies of diagnostic accuracy were included, encompassing 17,810 women (47) with 19,043 lesions (7,623 malignant). A hierarchical summary receiver operating characteristic model produced the following summary sensitivities and specificities: 0.86 [0.83, 0.88] / 0.87 [0.84, 0.88] for SWSmean, 0.83 [0.80, 0.85]/ 0.88 [0.86, 0.90] for SWSmax, 0.86 [0.74, 0.93]/ 0.81 [0.69, 0.89] for SWSmin, and 0.82 [0.77, 0.86] / 0.88 [0.85, 0.91] for SWSSD, respectively. By calculating and utilizing the resulting likelihood ratios, SWE was shown capable of downgrading BI-RADS 4a and upgrading BI-RADS 3 lesions. Thus, SWE has the potential to provide increased discriminative power in the diagnosis of breast cancer if used synergistically with mammography and B-mode ultrasound. Current society guidelines do not provide definitive recommendations about the role of SWE in screening and diagnosis, nor its counterpart strain elastography (SE). The literature suggests that a combination of SE and SWE may provide better discriminatory power than SWE alone and serve as an adjunct to current diagnostic techniques, opening an avenue for future study

Contrast-enhanced imaging in the biological and functional assessment of breast cancer

Contrast-enhanced MRI and ultrasound have emerged as additional imaging modalities in the management of breast cancer. This thesis examines the role these modalities currently play in the surgical management of breast cancer. Ways in which MRI may contribute to staging, diagnosis, treatment and prognosis are investigated. It was demonstrated that small additional enhancing foci on MRI, away from the primary tumour, represent in-situ or invasive cancer foci. Although their resection may result in extended wide local excisions or even unnecessary mastectomies, it was demonstrated that MRI findings do not currently influence the amount of tissue removed during breast conservation surgery. Volumetric analysis of breast MRI was proposed as an accurate objective assessment of the extent of surgery required for a particular tumour. Breast MRI was shown to be useful in the assessment of extent of residual disease during primary medical therapy but not in the detection of axillary lymph node metastases. In the second section of this thesis, the clinical application of pre-operative MRI in providing prognostic as well as diagnostic information was evaluated. Contrast- enhancement with both MRI and ultrasound is believed to depend on tumour angiogenesis but only a weak correlation was demonstrated between contrast- enhancement intensity and tumour angiogenesis. The detection of angiogenesis was applied to Doppler ultrasound using a novel microbubble ultrasound contrast agent (Levovist). Within a multicentre prospective study, Doppler ultrasound was shown to be a powerful discriminator of malignancy in suspected local recurrence. A strong correlation was found between MRI and histological assessment of tumour size but there was no correlation between enhancement intensity and other pathological prognostic variables. This thesis has shown that breast MRI is useful in pre-operative planning of surgery and provides diagnostic as well as limited prognostic information. Future proposed studies to determine the effect of MRI on patient management and patient outcome in breast cancer are considered

Fuzzy Analysis of Breast Cancer Disease using Fuzzy c-means and Pattern Recognition

Breast cancer is the second largest cause of cancer deaths among women. At the same time, it is also among the most curable cancer types if it can be diagnosed early. The automatic diagnosis of breast cancer is an important, real-world medical problem. In this article is introduced a new approach for diagnosis of breast cancer. The proposed approach uses Fuzzy c-means (FCM) algorithm and pattern recognition method. Algorithm has been applied to breast cancer clinic instances obtained from the University of Wisconsin. Using FCM algorithm clinic instances are grouped into two clusters, one with benign instances and other with malign instances. Further, input data are divided in train data and test data and success of each is evaluated. In pattern recognition method each input test data is assigned to one of the clusters obtained from the process of FCM classification. The proposed system has showed that the recommended system has a high accuracy

Comparative analysis of diagnostic performance, feasibility and cost of different test-methods for thyroid nodules with indeterminate cytology

Since it is impossible to recognize malignancy at fine needle aspiration (FNA) cytology in indeterminate thyroid nodules, surgery is recommended for all of them. However, cancer rate at final histology is < 30%. Many different test-methods have been proposed to increase diagnostic accuracy in such lesions, including Galectin-3-ICC (GAL-3-ICC), BRAF mutation analysis (BRAF), Gene Expression Classifier (GEC) alone and GEC+BRAF, mutation/fusion (M/F) panel, alone, M/F panel+miRNA GEC, and M/F panel by next generation sequencing (NGS), FDG-PET/CT, MIBI-Scan and TSHR mRNA blood assay. We performed systematic reviews and meta-analyses to compare their features, feasibility, diagnostic performance and cost. GEC, GEC+BRAF, M/F panel+miRNA GEC and M/F panel by NGS were the best in ruling-out malignancy (sensitivity = 90%, 89%, 89% and 90% respectively). BRAF and M/F panel alone and by NGS were the best in ruling-in malignancy (specificity = 100%, 93% and 93%). The M/F by NGS showed the highest accuracy (92%) and BRAF the highest diagnostic odds ratio (DOR) (247). GAL-3-ICC performed well as rule-out (sensitivity = 83%) and rule-in test (specificity = 85%), with good accuracy (84%) and high DOR (27) and is one of the cheapest (113 USD) and easiest one to be performed in different clinical settings. In conclusion, the more accurate molecular-based test-methods are still expensive and restricted to few, highly specialized and centralized laboratories. GAL-3-ICC, although limited by some false negatives, represents the most suitable screening test-method to be applied on a large-scale basis in the diagnostic algorithm of indeterminate thyroid lesions