Automatic Detection of Microcalcifications in Digitized Mammograms using Fuzzy 2-Partition Entropy and Mathematical Morphology

Image segmentation has been used widely for detection and extraction of objects in digital images. Thresholding is one of the effective image segmentation techniques. Fuzzy 2-partition entropy is used effectively for the selection of threshold value. Fuzzy 2-partition entropy required the optimization of some parameters for the selection of threshold value. Teaching-Learning-Based Optimization (TLBO) algorithm has been applied on fuzzy 2-partition entropy to find these parameters and subsequent threshold value. Normally, fuzzy 2-partition Shannon entropy is used for thresholding. In the proposed research work, fuzzy 2-partition Kapur entropy is explored for the evaluation of its potential for selecting optimal threshold value using TLBO algorithm. The present work measures the performance comparison of fuzzy 2-partition Kapur entropy using TLBO with fuzzy 2-partition Shannon entropy using TLBO. The experiments are carried out on standard test images from benchmark dataset. Peak signal to noise ratio, uniformity and structural similarity index are the three different measures which have been used to compare the performance. Results demonstrate that the performance of fuzzy 2-partition Kapur entropy using TLBO is quite promising.

Microcalcification (MC) clusters in mammograms are one of the primary signs of breast cancer. In the literature, most MC detection methods follow a two-step paradigm: segmenting each MC and analyzing their spatial distributions to form MC clusters. However, segmentation of MCs cannot avoid low sensitivity or high false positive rate due to their variability in size (sometimes <0.1 mm), brightness, and shape (with diverse surroundings). In this paper, we propose a novel self-adversarial learning framework to differentiate and delineate the MC clusters in an end-to-end manner. The class activation mapping (CAM) mechanism is employed to directly generate the contours of MC clusters with the guidance of MC cluster classification and box annotations. We also propose the self-adversarial learning strategy to equip CAM with better detection capability of MC clusters by using the backbone network itself as a discriminator. Experimental results suggest that our method can achieve better performance for MC cluster detection with the contouring of MC clusters and classification of MC types.

Mammography and finding of suspicious masses during self-examinations and clinical breast examinations form the primary screening tools for early detection of breast cancer. Mammography is essential for early detection of cancer, prior to any other means. Many abnormalities other than cancer are shown by screening mammography, leading to biopsy of the suspicious defect. Any enhancement that reduces the number of unnecessary biopsies is welcomed. Digital enhancement may aid in early detection of some patterns, such as microcalcification clusters, indicating the onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers, and early detection may enable a complete cure. The individual calcifications are hard to detect, due to their size and shape variability and the inhomogeneous background structure. Our study addresses only early detection of microcalcifications. We present an algorithm which locates microcalcifications based on local gray-scale variability, tissue structures and image statistics. The mammographs are digitally enhanced to accent textures and a previously developed threshold filter creates a binary mask of the calcification's spatial location.

Mammography is the only technique currently used for detecting microcalcification (MC) clusters, an early indicator of breast cancer. However, mammographic images superimpose a three-dimensional compressed breast image onto two-dimensional projection views, resulting in overlapped anatomical breast structures that may obscure the detection and visualization of MCs. One possible solution to this problem is the use of cone beam computed tomography (CBCT) with a flat-panel (FP) digital detector. Although feasibility studies of CBCT techniques for breast imaging have yielded promising results, they have not shown how radiation dose and x-ray tube voltage affect the accuracy with which MCs are detected by CBCT experimentally. We therefore conducted a phantom study using a FP-based CBCT system with various mean glandular doses and kVp values. An experimental CBCT scanner was constructed with a data acquisition rate of 7.5 frames/s. 10.5 and 14.5 cm diameter breast phantoms made of gelatin were used to simulate uncompressed breasts consisting of 100% glandular tissue. Eight different MC sizes of calcium carbonate grains, ranging from 180-200 microm to 355-425 microm, were used to simulate MCs. MCs of the same size were arranged to form a 5 x 5 MC cluster and embedded in the breast phantoms. These MC clusters were positioned at 2.8 cm away from the center of the breast phantoms. The phantoms were imaged at 60, 80, and 100 kVp. With a single scan (360 degrees), 300 projection images were acquired with 0.5 x, 1x, and 2x mean glandular dose limit for 10.5 cm phantom and with 1x, 2x, and 4x for 14.5 cm phantom. A Feldkamp algorithm with a pure ramp filter was used for image reconstruction. The normalized noise level was calculated for each x-ray tube voltage and dose level. The image quality of the CBCT images was evaluated by counting the number of visible MCs for each MC cluster for various conditions. The average percentage of the visible MCs was computed and plotted as a function of the MGD, the kVp, and the average MC size. The results showed that the MC visibility increased with the MGD significantly but decreased with the breast size. The results also showed that the x-ray tube voltage affects the detection of MCs under different circumstances. With a 50% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 348(+/-2), 288(+/-7), 257(+/-2) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 355 (+/-1), 307 (+/-7), 275 (+/-5) microm at 6, 12, and 24 mGy, respectively. With a 75% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 367 (+/-1), 316 (+/-7), 265 (+/-3) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 377 (+/-3), 334 (+/-5), 300 (+/-2) microm at 6, 12, and 24 mGy, respectively.

Background Earlier detection of breast cancer through mammography screening has reduced disease-specific mortality; however, confounding issues such as technical challenges, breast density, and tumor size can result in false negatives and ultimately later stage diagnosis. Next generation liquid biopsy has the potential to complement mammography and enable earlier detection for more women. We have previously demonstrated high sensitivity and specificity for early detection of invasive breast cancer (IBC) by utilizing a novel category of cancer-associated small RNAs, termed orphan noncoding RNAs (oncRNAs), through a liquid biopsy platform. Here, we further improve the ability to detect breast cancer in a larger, multi-source cohort through an AI-driven approach and demonstrate potential for detection of ductal carcinoma in-situ (DCIS). Methods We utilized The Cancer Genome Atlas (TCGA) small RNA-seq database to discover a library of 20,538 oncRNAs, through a female-specific analysis, that were significantly enriched among 1,103 breast tumors compared to 349 normal tissue samples spanning multiple tissue sites. The diagnostic performance of these oncRNAs were assessed in an independent cohort of archived serum samples from 663 female individuals, sourced from Indivumed (Hamburg, Germany), Proteogenex (Inglewood, CA), and MT Group (Los Angeles, CA), including 279 breast cancer patients of various stages (221 IBC and 58 DCIS; mean age: 57.0 ± 13.8 years; ever-smoker: 25.8%) and 304 age-matched controls (mean age: 58.5 ± 13.9 years; ever-smoker: 23.4%) without breast cancer. All samples were collected between 2010–2022 at time of diagnosis for breast cancer patients. We sequenced the small RNA content of these samples at an average depth of 25.28 ± 9.37 million 50-bp single-end reads. We detected 18,025 (87.8%) unique breast cancer-specific oncRNA species within at least one sample from the study cohort. We then trained a generative AI model using 5-fold cross-validation to predict cancer status for all samples. Results Our oncRNA-based model achieved an overall AUC of 0.95 (95% CI, 0.93–0.97) for prediction of IBC versus cancer-free controls with a sensitivity of 0.87 (0.82–0.91) at 90% specificity. We observed high sensitivities, also at 90% specificity, across all tumor stages and tumor sizes (Table 1). Sensitivities for the earliest stage and smallest tumor size were 0.87 (0.78–0.93) and 0.81 (0.61–0.93) for Stage I (n=83) and T1a–b ( &amp;gt;1mm to ≤10mm; n=26), respectively. Additionally, in a small single-source cohort, we also saw high model accuracy and sensitivity for DCIS, which we aim to confirm in additional cohorts. While our overall cancer cohort primarily consisted of individuals with luminal breast cancer, our model had high sensitivities across all breast cancer subtypes at 0.90 (0.84–0.94), 0.73 (0.59–0.85), and 0.86 (0.42–1.0) for luminal (n=181), HER2 positive (n=49), and triple negative (n=7), respectively. Conclusions We further demonstrate the potential utility of oncRNAs as a blood-based biomarker using an AI algorithm for sensitive and accurate early detection of breast cancer in a large cohort. Additionally, we have shown that this oncRNA-based assay performs well in detecting small, early-stage invasive breast tumors, with potential to detect precursors of breast cancer. Table 1: Model sensitivity in breast cancer by tumor stage and size For each tumor stage and size, as defined by the AJCC 7th Edition breast cancer staging system, sensitivity and 95% Pearson-Clopper confidence intervals (CI) are reported at 90% specificity for the number of samples (N). Citation Format: Noura Tbeileh, Taylor Cavazos, Mehran Karimzadeh, Jeffrey Wang, Alice Huang, Dung Ngoc Lam, Seda Kilinc, Jieyang Wang, Xuan Zhao, Andy Pohl, Helen Li, Lisa Fish, Kimberly Chau, Marra Francis, Lee Schwartzberg, Patrick Arensdorf, Hani Goodarzi, Fereydoun Hormozdiari, Babak Alipanahi. Cell-free orphan noncoding RNAs and AI enable early detection of invasive breast cancer and ductal carcinoma in-situ [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO2-13-08.

Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called <i>relevance vectors</i>. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

Simple SummaryBreast cancer screening is associated with benefits, such as mortality reduction and improved quality of life, and harms, such as false-positive results, overdiagnoses, and costs. Novel screen tests could be considered to reduce the harms and increase the benefits of screening. Liquid biopsies have been proposed as a novel method for the early detection of breast cancer. However, studies show that liquid biopsies based on cell-free DNA have a low sensitivity for early-stage breast cancer. Using the microsimulation model MISCAN-Fadia, we model the benefits, harms, and costs of the early detection of breast cancer using liquid biopsies for varying levels of liquid biopsy sensitivity and specificity. We found that liquid biopsies are unlikely to be an alternative to digital mammography, given the test performance based on a CCGA substudy. When liquid biopsies are unable to detect the precursor lesion of breast cancer—ductal carcinoma in situ (DCIS)—they need to be able to detect small, early-stage tumors, with high specificity, at low costs in order to be an alternative to digital mammography. We estimated a maximum liquid biopsy price of USD 187, which is substantially lower than currently listed prices.Breast cancer screening is associated with harms, such as false-positives and overdiagnoses, and, thus, novel screen tests can be considered. Liquid biopsies have been proposed as a novel method for the early detection of cancer, but low cell-free DNA tumor fraction might pose a problem for the use in population screening. Using breast cancer microsimulation model MISCAN-Fadia, we estimated the outcomes of using liquid biopsies in breast cancer screening in women aged 50 to 74 in the United States. For varying combinations of test sensitivity and specificity, we quantify the impact of the use of liquid biopsies on the harms and benefits of screening, and we estimate the maximum liquid biopsy price for cost-effective implementation in breast cancer screening at a cost-effectiveness threshold of USD 50,000. We investigate under what conditions liquid biopsies could be a suitable alternative to digital mammography and compare these conditions to a CCGA substudy. Outcomes were compared to digital mammography screening, and include mortality reduction, overdiagnoses, quality-adjusted life-years (QALYs), and the maximum price of a liquid biopsy for cost-effective implementation. When liquid biopsies are unable to detect DCIS, a large proportion of overdiagnosed cases is prevented but overall breast cancer mortality reduction and quality of life are lower, and costs are higher compared to digital mammography screening. Liquid biopsies prices should be restricted to USD 187 per liquid biopsy depending on test performance. Overall, liquid biopsies that are unable to detect ductal carcinoma in situ (DCIS) need to be able to detect small, early-stage tumors, with high specificity, at low costs in order to be an alternative to digital mammography. Liquid biopsies might be more suitable as an addition to digital mammography than as an alternative.

This paper presents a machine learning based approach for the discrimination of malignant and benign microcalcification (MC) clusters in digital mammograms. A series of morphological operations was carried out to facilitate the feature extraction from segmented microcalcification. A combination of morphological, texture, and distribution features from individual MC components and MC clusters were extracted and a correlation-based feature selection technique was used. The clinical relevance of the selected features is discussed. The proposed method was evaluated using three different databases: Optimam Mammography Image Database (OMI-DB), Digital Database for Screening Mammography (DDSM), and Mammographic Image Analysis Society (MIAS) database. The best classification accuracy (%) was achieved for OPTIMAM using a stack generalization classifier with 10-fold cross validation obtaining an A value equal to .

We propose computer-aided detection (CADe) algorithm for microcalcification (MC) clusters in reconstructed digital breast tomosynthesis (DBT) images. The algorithm consists of prescreening, MC detection, clustering, and false-positive (FP) reduction steps. The DBT images containing the MC-like objects were enhanced by a multiscale Hessian-based three-dimensional (3D) objectness response function and a connected-component segmentation method was applied to extract the cluster seed objects as potential clustering centers of MCs. Secondly, a signal-to-noise ratio (SNR) enhanced image was also generated to detect the individual MC candidates and prescreen the MC-like objects. Each cluster seed candidate was prescreened by counting neighboring individual MC candidates nearby the cluster seed object according to several microcalcification clustering criteria. As a second step, we introduced bounding boxes for the accepted seed candidate, clustered all the overlapping cubes, and examined. After the FP reduction step, the average number of FPs per case was estimated to be 2.47 per DBT volume with a sensitivity of 83.3%.

Clustered microcalcifications (MCs) in mammograms are an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we integrated the possibilistic fuzzy c-means (PFCM) clustering algorithm and weighted support vector machine (WSVM) for the detection of MC clusters in full-field digital mammograms (FFDM). For each image, suspicious MC regions are extracted with region growing and active contour segmentation. Then geometry and texture features are extracted for each suspicious MC, a mutual information-based supervised criterion is used to select important features, and PFCM is applied to cluster the samples into two clusters. Weights of the samples are calculated based on possibilities and typicality values from the PFCM, and the ground truth labels. A weighted nonlinear SVM is trained. During the test process, when an unknown image is presented, suspicious regions are located with the segmentation step, selected features are extracted, and the suspicious MC regions are classified as containing MC or not by the trained weighted nonlinear SVM. Finally, the MC regions are analyzed with spatial information to locate MC clusters. The proposed method is evaluated using a database of 410 clinical mammograms and compared with a standard unweighted support vector machine (SVM) classifier. The detection performance is evaluated using response receiver operating (ROC) curves and free-response receiver operating characteristic (FROC) curves. The proposed method obtained an area under the ROC curve of 0.8676, while the standard SVM obtained an area of 0.8268 for MC detection. For MC cluster detection, the proposed method obtained a high sensitivity of 92 % with a false-positive rate of 2.3 clusters/image, and it is also better than standard SVM with 4.7 false-positive clusters/image at the same sensitivity.

Prélèvement de ganglions sentinelles dans les carcinomes intracanalaires du sein (± micro-invasion)

Breast cancer is the most common cancer among American women, except for skin cancers. About 12 % women in the United States will develop invasive breast cancer during their lifetime. Currently one of the most accepted model/theories is that ductal breast cancer (most common type of breast cancer) follows a linear progression: from normal breast epithelial cells to ductal hyperplasia to atypical ductal hyperplasia (ADH) to ductal carcinoma in situ (DCIS), and finally to invasive ductal carcinoma (IDC). Distinguishing pure ADH diagnosis from DCIS and/or IDC on mammography, and even combined with follow-up core needle biopsy (CNB) is still a challenge. Therefore subsequent surgical excision cannot be avoided to make a definitive diagnosis. MicroRNAs (miRNAs) are a highly abundant class of endogenous non-coding RNAs, which contribute to cancer initiation and progression, and are differentially expressed between normal and cancer tissues. They can function as either tumor suppressors or oncogenes. With accumulating evidence of the role of miRNAs in breast cancer progression, including our own studies, we sought to summarize the nature of early breast lesions and the potential use of miRNA molecules as biomarkers in early breast cancer detection. In particular, miRNA biomarkers may potentially serve as a companion tool following mammography screening and CNB. In the long-term, a better understanding of the molecular mechanisms underlying the miRNA signatures associated with breast cancer development could potentially result in the development of novel strategies for disease prevention and therapy.

In this paper, we propose a novel method for the detection of microcalcifications using mathematical morphology and a support vector machine (SVM). First, the contrast in the original mammogram was improved by gamma correction and two carefully designed structural elements were used to enhance any microcalcifications. Next, the potential regions were extracted using our proposed dual-threshold technique. Finally, a SVM classifier was used to reduce the number of false positives. The performance of the proposed method was evaluated using the MIAS database. The experimental results demonstrated the efficiency and effectiveness of our method.

Detection of microcalcifications using nonlinear beamforming techniques.

This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.