Number Of Unnecessary Breast Biopsies Research Articles

Automatic Classification of Cancerous Masses in Digital Mammograms Using Curvelet Analysis and Hybrid Genetic Algorithm-Artificial Neural Network Model

Background: Mammography is the most fundamental and widely used method for detecting breast abnormalities. Distinguishing malignant from benign lesions requires extracting relevant information, which can be challenging and time-consuming for radiologists. Computer-aided diagnosis (CAD) techniques can serve as complementary diagnostic tools, assisting radiologists in the early detection and analysis of abnormalities in mammograms. Objectives: This study aimed to propose a CAD system for extracting significant features of abnormalities in breast mammograms using Curvelet transform and fractal analysis, and classifying breast tumors as malignant or benign based on the calculated features. Patients and Methods: In this study, an efficient feature extraction method was applied, utilizing Curvelet transform and fractal analysis, on a dataset comprising 113 abnormal images from the Mammographic Image Analysis Society (MIAS) database, which included 62 benign and 51 malignant cases. The method yielded 575 features, but due to potential irrelevance or redundancy, a multi-objective optimization (MOO) approach based on a genetic algorithm (GA) for an artificial neural network (ANN), named GA-MOO-ANN, was proposed to obtain and focus on an optimal and effective feature set. As a result of this approach, a set of 17 efficient features was extracted. The proposed algorithm was implemented in MATLAB 2014a, and the performance metrics were calculated using 6-fold cross-validation. Results: The experimental results demonstrated exceptional performance, with an accuracy (Acc) of 98.2%, specificity (Sp) of 100%, sensitivity (Se) of 96.8%, positive predictive value (PPV) of 100%, negative predictive value (NPV) of 96.2%, and an impressive area under the curve (AUC) of 0.98, providing comparable results to other recent methods. Conclusion: The current findings suggest that the proposed method could be a valuable tool for breast cancer diagnosis, potentially reducing the number of unnecessary breast biopsies. This method may lead to more efficient patient evaluation and earlier detection of breast tumors.

Feasibility of Diffusion Tensor Imaging for Decreasing Biopsy Rates in Breast Imaging: Interim Analysis of a Prospective Study.

Because of the limited specificity of diagnostic imaging, many breast lesions referred for biopsy turn out to be benign. The objective of this study was to evaluate whether diffusion tensor MRI (DTI) parametric maps can be used to safely avoid biopsy of breast lesions. Individuals referred for breast biopsy based on mammogram (MG), ultrasound (US), and/or contrast enhanced (CE)-MRI were recruited. Scans consisting of T2-weighted and DTI sequences were performed. Multiple DTI-derived parametric color maps were evaluated semi-quantitatively to characterize lesions as "definitely benign," "not definitely benign," or "suspicious." All patients subsequently underwent biopsy. In this moderately-sized prospective study, 21 out of 47 pathologically proven benign lesions were characterized by both readers as "definitely benign," which would have precluded the need for biopsy. Biopsy was recommended for 11 out of 13 cancers that were characterized as "suspicious." In the remaining two cancers and 26 of 47 benign lesions, the scans were characterized as "not definitely benign" and hence required biopsy. The main causes for "not definitely benign" scans were small lesion sizes and noise. The results suggest that in appropriately selected patients, DTI may be used to safely reduce the number of unnecessary breast biopsies.

Abstract PD11-05: Intelligent shear-wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): An international, multicenter analysis

Abstract Background: Breast ultrasound identifies additional carcinomas not detected in mammography, but has a higher rate of false-positive findings which result in more unnecessary breast biopsies. Shear-Wave Elastography (SWE), an ultrasound technique used to quantify the stiffness of a lesion, showed promising results to improve the diagnostic performance of B-mode breast ultrasound but also to miss some cancers. As the stiffness of a lesion is found to be influenced by individual patient characteristics, incorporation of lesion stiffness in more individualized assessments may be key to the problem of reducing unnecessary breast biopsies without impairing the breast cancer detection rate. Thus, in this study, we evaluated whether an intelligent algorithm incorporating traditional SWE values as well as other patient and clinical variables (hereafter “intelligent SWE”) could reduce the number of unnecessary breast biopsies without impairing the breast cancer detection rate compared to traditional SWE and B-mode breast ultrasound for patients with suspicious breast lesions. Methods: We trained, tested, and validated machine learning algorithms using patient, clinical, ultrasound, and SWE information to classify breast masses. We used international, multicenter data from 857 women with BI-RADS 4 breast masses at 12 study sites in 7 countries. Patients underwent B-mode breast ultrasound, SWE, and subsequent histopathologic evaluation. 10-fold cross-validation was used to train and test the algorithms on data from 11 of the 12 sites which were further validated using the additional site’s data. The results of B-mode breast ultrasound, traditional SWE, and intelligent SWE were compared to the gold standard of histopathologic evaluation. We calculated sensitivity, specificity, and AUROC and used McNemar tests to test for significant differences in diagnostic performance. Results: The mean age was 49.5 years (SD 16.3) and 42.2% breast masses (n=362 of 857) were found to be malignant as confirmed by histopathology. In the external validation set (n=285), traditional SWE showed a significantly higher diagnostic performance compared to B-mode breast ultrasound (P < 0.001), whereas intelligent SWE outperformed both B-mode breast ultrasound and traditional SWE (P < 0.001). The neural network algorithm showed a significantly higher diagnostic performance compared to the Logistic Regression with Elastic Net Penalty (P = 0.004). The neural network algorithm achieved a sensitivity of 100% (95% CI 97.1 to 100%, 126 of 126) and a specificity of 50.3% (95% CI 42.3 to 58.3%, 80 of 159); the number of unnecessary biopsies were reduced by 50.3% (79 vs. 159) without missing any cancer compared to B-mode breast ultrasound. Model-agnostic variable importance plots to provide insights into the model predictions showed that the three most important variables for intelligent SWE were patient age followed by Shear-Wave velocity and orientation of the lesion (parallel vs. not parallel) in B-mode ultrasound. Conclusion: This is the first evidence which suggests that the majority of false-positive breast biopsies could be safely avoided by using intelligent SWE without impairing breast cancer detection rates. These results may be helpful in their ability to reduce treatment burden for patients, providers, and healthcare systems. Trial registration: NCT02638935. Funding: Siemens Medical Solutions USA, Inc Diagnostic Performance ComparisonB-mode Breast UltrasoundTraditional Shear-Wave ElastographyIntelligent Shear-Wave Elastography – Logistic Regression with Elastic Net PenaltyIntelligent Shear-Wave Elastography – neural networkAUROC – value (95% CI)–0.84 (0.79-0.89)0.93 (0.90-0.95)0.93 (0.90-0.96)Sensitivity – % (95% CI); no.100% (97.1-100%); 126 of 12697.6% (93.2-99.5%); 123 of 126100% (97.1-100%); 126 of 126100% (97.1-100%); 126 of 126Specificity – % (95% CI); no.0% (0.0-2.3%); 0 of 15923.9% (17.5-31.3%); 38 of 15936.5% (29.0-44.5%); 58 of 15950.3% (42.3-58.3%); 80 of 159Negative predictive value – % (95% CI); no.–92.7% (80.1-98.5%); 38 of 41100% (93.8-100%); 58 of 58100% (95.5-100%); 80 of 80Positive predictive value – % (95% CI); no.44.2% (38.4-50.2); 126 of 28550.4% (44.0-56.8%); 123 of 24455.5% (48.8-62.1%); 126 of 22761.5% (54.4-68.2%); 126 of 205 Citation Format: André Pfob, Chris Sidey-Gibbons, Richard G. Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk-André Clevert, Sarah Fastner, Christina Gomez, Manuela Goncalo, Ines Gruber, Markus Hahn, André Hennigs, Chi Ho, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Joerg Heil, Michael Golatta. Intelligent shear-wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): An international, multicenter analysis [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-05.

The potential of combined shear wave and strain elastography to reduce unnecessary biopsies in breast cancer diagnostics – An international, multicentre trial

BackgroundShear wave elastography (SWE) and strain elastography (SE) have shown promising potential in breast cancer diagnostics by evaluating the stiffness of a lesion. Combining these two techniques could further improve the diagnostic performance. We aimed to exploratorily define the cut-offs at which adding combined SWE and SE to B-mode breast ultrasound could help reclassify Breast Imaging Reporting and Data System (BI-RADS) 3-4 lesions to reduce the number of unnecessary breast biopsies. MethodsWe report the secondary results of a prospective, multicentre, international trial (NCT02638935). The trial enrolled 1288 women with BI-RADS 3 to 4c breast masses on conventional B-mode breast ultrasound. All patients underwent SWE and SE (index test) and histopathologic evaluation (reference standard). Reduction of unnecessary biopsies (biopsies in benign lesions) and missed malignancies after recategorising with SWE and SE were the outcome measures. ResultsOn performing histopathologic evaluation, 368 of 1288 breast masses were malignant. Following the routine B-mode breast ultrasound assessment, 53.80% (495 of 920 patients) underwent an unnecessary biopsy. After recategorising BI-RADS 4a lesions (SWE cut-off ≥3.70 m/s, SE cut-off ≥1.0), 34.78% (320 of 920 patients) underwent an unnecessary biopsy corresponding to a 35.35% (320 versus 495) reduction of unnecessary biopsies. Malignancies in the new BI-RADS 3 cohort were missed in 1.96% (12 of 612 patients). ConclusionAdding combined SWE and SE to routine B-mode breast ultrasound to recategorise BI-RADS 4a patients could help reduce the number of unnecessary biopsies in breast diagnostics by about 35% while keeping the rate of undetected malignancies below the 2% ACR BI-RADS 3 definition.

Sınıflandırma Algoritmalarını Kullanarak Meme Dokusunda Kitleleri Değerlendirmeye Yönelik Karar Destek Sistemi

Breast cancer is the most widely recognized cancer-related death among women globally. Epidemiological studies released in different parts of the world over the past two decades show a significant rise in mortality rates for breast cancer. Today, mammography is the most effective method for imaging masses and microcalcifications in breast tissue. On the other hand, breast biopsy predictions arising from mammogram analysis lead to nearly 70 percent biopsies of benign findings that can be prevented without a biopsy. An automated method is therefore required to assist physicians in mammography analysis prognoses. Researchers have suggested different medical decision support systems recently. In this study, a medical decision support system to be utilized in the process of a breast cancer diagnosis is proposed. The primary purpose of this system is to lower the number of unnecessary breast biopsies and make the diagnosis more reliable. Accordingly, apart from the age of the patient, BI-RADS assessment of the breast tissue, the shape of the mass, mass margin, tissue density, the class label indicating the severity of the lesion are evaluated using the performances of a Naive Bayes algorithm , which is a probabilistic classification algorithm, and Multilayer Perceptron algorithm, which is a feed forward neural network, as two different classification algorithms via a preferred dataset in which each mammography mass have six different feature. The proposed system can help to make a biopsy or short-time follow-up decision. The test results are promising that the proposed method can be used as a decision module in computer-aided diagnosis systems.

A Simple Ultrasound Based Classification Algorithm Allows Differentiation of Benign from Malignant Breast Lesions by Using Only Quantitative Parameters

PurposeWe hypothesized that different quantitative ultrasound (US) parameters may be used as complementary diagnostic criteria and aimed to develop a simple classification algorithm to distinguish benign from malignant breast lesions and aid in the decision to perform biopsy or not.ProceduresOne hundred twenty-four patients, each with one biopsy-proven, sonographically evident breast lesion, were included in this prospective, IRB-approved study. Each lesion was examined with B-mode US, Color/Power Doppler US and elastography (Acoustic Radiation Force Impulse–ARFI). Different quantitative parameters were recorded for each technique, including pulsatility (PI) and resistive Index (RI) for Doppler US and lesion maximum, intermediate, and minimum shear wave velocity (SWVmax, SWVinterm, and SWVmin) as well as lesion-to-fat SWV ratio for ARFI. Receiver operating characteristic curve (ROC) analysis was used to evaluate the diagnostic performance of each quantitative parameter. Classification analysis was performed using the exhaustive chi-squared automatic interaction detection method. Results include the probability for malignancy for every descriptor combination in the classification algorithm.ResultsSixty-five lesions were malignant and 59 benign. Out of all quantitative indices, maximum SWV (SWVmax), and RI were included in the classification algorithm, which showed a depth of three ramifications (SWVmax ≤ or > 3.16; if SWVmax ≤ 3.16 then RI ≤ 0.66, 0.66–0.77 or > 0.77; if RI ≤ 0.66 then SWVmax ≤ or > 2.71). The classification algorithm leads to an AUC of 0.887 (95 % CI 0.818–0.937, p < 0.0001), a sensitivity of 98.46 % (95 % CI 91.7–100 %), and a specificity of 61.02 % (95 % CI 47.4–73.5 %). By applying the proposed algorithm, a false-positive biopsy could have been avoided in 61 % of the cases.ConclusionsA simple classification algorithm incorporating two quantitative US parameters (SWVmax and RI) shows a high diagnostic performance, being able to accurately differentiate benign from malignant breast lesions and lower the number of unnecessary breast biopsies in up to 60 % of all cases, avoiding any subjective interpretation bias.

Probabilistic neural network for breast cancer classification

Among cancers, breast cancer causes second most number of deaths in women. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis systems have been proposed in the last years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short-term follow-up examination instead. In clinical diagnosis, the use of artificial intelligent techniques as neural networks has shown great potential in this field. In this paper, three classification algorithms, multi-layer perceptron (MLP), radial basis function (RBF) and probabilistic neural networks (PNN), are applied for the purpose of detection and classification of breast cancer. Decision making is performed in two stages: training the classifiers with features from Wisconsin Breast Cancer database and then testing. The performance of the proposed structure is evaluated in terms of sensitivity, specificity, accuracy and ROC. The results revealed that PNN was the best classifiers by achieving accuracy rates of 100 and 97.66 % in both training and testing phases, respectively. MLP was ranked as the second classifier and was capable of achieving 97.80 and 96.34 % classification accuracy for training and validation phases, respectively, using scaled conjugate gradient learning algorithm. However, RBF performed better than MLP in the training phase, and it has achieved the lowest accuracy in the validation phase.

The prediction of breast cancer biopsy outcomes using two CAD approaches that both emphasize an intelligible decision process

Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value < 0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z) = 0.89 +/- 0.01, the decision-tree approach in A(z) = 0.87 +/- 0.01, and the ANN approach in A(z) = 0.88 +/- 0.01.

Breast scintigraphy today: indications and limitations.

Breast carcinoma is the most common neoplasm found among women in the Western world. Mammography (MM) is the most widely used diagnostic imaging method for screening and diagnosing breast cancer. However, despite technical improvements in recent years, MM has known diagnostic limits; consequently not all breast carcinomas are identified on mammograms, especially if the breast is dense, there is a breast prosthesis or the patient has previously undergone radiation, surgery or biopsy. In addition, the mammographic images of benign and malignant lesions can be similar. Therefore, abnormalities detected on MM frequently result in negative biopsies. Scintimammography (SM) is the functional imaging study of the breast using primarily the radiopharmaceuticals (99m)Tc-sestamibi and (99m)Tc-tetrofosmin. The main advantage of SM is that its functional basis makes this technique a useful complement to MM. SM resolves some of the main limitations of MM as it is not affected by changes in breast morphology. Several single-site and multi-centre studies have demonstrated that SM has an improved specificity compared with MM, because it is better able to distinguish malignant from benign breast lesions. Interestingly, except in smaller lesions, a higher sensitivity has been recorded for SM than for MM in most of these studies as well. Adjunctive use of SM when MM is equivocal can reduce the number of unnecessary breast biopsies and identify previously unexpected sites of breast cancer. SM appears unaffected by the anatomical changes seen following chemotherapy and radiotherapy, and so this technique can be particularly useful in monitoring the treatment of breast cancer patients, especially when breast-conserving treatment is given. The main limitation to SM has been the sub-optimal resolution of the standard Anger gamma camera, which makes it difficult to detect lesions of less than 10 mm; however, the development of high-resolution breast-dedicated gamma cameras may offer improvements in this respect. This review will look at the evidence for SM and show how it can become part of the clinical care algorithm in breast cancer.

Scintimammography with Tc-99m MIBI in patients with suspicion of primary breast cancer

The aim of the study was to evaluate the diagnostic accuracy of scintimammography with Tc-99m MIBI for the detection of breast cancer. Patients with a suspicious lesion detected by palpation or mammography were included in the study. Excisional biopsy was performed on all patients. Mammography was performed within 3 weeks prior to scintigraphy. All patients received 740 MBq Tc-99m MIBI intravenously in the arm contralateral to the suspicious breast and were subsequently examined in a prone position. At 5 to 10 min postinjection, planar images were obtained in lateral and anterior views, with an acquisition time of 10 min each. After planar imaging, a SPECT study was performed using a two-head camera. Breast cancer was confirmed in 29 out of 68 patients. The tumor size ranged from 6 to 90 mm in diameter. For scintigraphic studies, the overall sensitivity and specificity was 83% and 84%, respectively. However, sensitivity for palpable lesions was 100%. The smallest detectable tumor measured 9 mm in diameter and could be visualized only in the planar scintigram. Using Tc-99m MIBI, axillary lymph node metastases could be detected with a sensitivity of 82%. Scintigraphy with Tc-99m MIBI has a high diagnostic accuracy for the detection of primary breast cancer in patients with a palpable mass. Scintimammography may be used as a complementary method to mammography and help to decrease the number of unnecessary breast biopsies.

Farbdopplersonographische Dignitätsbeurteilung von Mammatumoren

Eighty-seven women with mammographically suspicious breast lesions were investigated prior to surgery. 32 breast carcinomas and 55 benign lesions were evaluated for the resistance index, the pulsatility index, the flow velocity and the acceleration index. Our study showed the resistance index with a threshold value of 0.70 to be best suited to differentiate benign tumors from malignant ones. In this series, the sensitivity of color Doppler ultrasonography for the detection of breast carcinomas was 84%, and the specificity 80%. The positive predictive value was 71% and the negative predictive value 90%. Color Doppler scanning offers one possible method for further investigation of patients with mammographic abnormalities and can help to reduce the number of unnecessary breast biopsies.

Mammographic abnormalities and the detection of carcinoma of the breast

In an attempt to reduce the number of unnecessary breast biopsies, the yield rate of cancer relative to specific types of mammographically detected abnormality was investigated. Between 1989 and 1991, 169 patients with an abnormality as detected by mammogram but with no palpable mass were reviewed. Abnormalities detected by mammogram were divided into four types. The rate of biopsy-confirmed cancer per type of abnormality was calculated. The incidence of cancer per type of mammographically detected abnormality was as follows: mass 11%, mass with abnormal calcification 21%, abnormal calcification 22%, and asymmetric density 2%. Biopsy is recommended for mammographically detected abnormalities of a mass, a mass with abnormal calcification, and abnormal calcification without mass. Consideration should be given to deferring biopsy in favor of a follow-up study for asymmetric densities.