Detection of cluster of microcalcifications based on watershed segmentation algorithm

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.

Background: Our 2004 survey of breast cancer survivors in the Katowice region (Poland) showed that the detection of the disease was triggered by self-examination in 58.9%, mammography in 19.2%, and clinical examination in 19.7% of cases. The purpose of the current study (2019/2020) was to determine if the implementation of national screening (mammography) in 2007 resulted in an increase of the relative contribution of mammography to detection of cancer. Methods: Subjects were 215 breast cancer patients, members of self-support groups in Katowice region. The questionnaire included questions on early detection of breast cancer, participation in screening, and socio-economic status. Results: Early detection of cancer was initiated by self-examination in 63.7%, mammography in 22.8%, clinical examination in 13.5% of subjects. Age at detection depended on the method (p < 0.001): 54.1 ± 10.8 years for self-examination, 60.0 ± 7.6 years for mammography, and 58.7 ± 10.8 years for clinical examination. Conclusions: Both in 2004 and 2020 self-examination is the most frequent method of early detection of breast cancer in the study area. The contribution of mammography remains on a low level (23%). This finding could be explained by a low participation in screening and by age of 50 years used as the entry criterion to national screening of breast cancer in Poland.

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.

Background: The existence of Higher Education has three main tasks, namely carrying out education and teaching, research, and community service. These three things constitute the Tridharma of Higher Education. In Indonesia, breast cancer is the second most common cancer suffered by women after oral/cervical cancer. Breast cancer generally attacks women who are over 40 years old. However, young women can get this cancer. To find early symptoms of breast cancer can be detected early by women themselves. Women can routinely perform the Breast Self-Examination (BSE) method by massaging and feeling around their breasts to find out whether or not there are lumps around the breasts. BSE is an effective way to detect breast cancer as early as possible.&#x0D; Objective: Carry out community service by conducting activities to teach breast self-examination (BSE) to young women.&#x0D; Method: This community service activity consists of counseling related to information on early detection of breast cancer and followed by a demonstration of breast self-examination (BSE) using the Breast Phantom tool.&#x0D; Result: Counseling and early detection of breast cancer was attended by 45 students of Madrasah Aliyah. Most students do not know about early detection of breast cancer and how to check BSE. After being given information about BSE, adolescents felt satisfied and committed to be able to carry out early detection of breast cancer.&#x0D; Conclusion: Community service activities have been carried out regarding information on early detection of breast cancer in young women.

Clustered microcalcifications are often the first sign of breast cancer in a mammogram. Nevertheless, all clustered microcalcifications are not found by an individual radiologist reading a mammogram. The use of a second reader may find those clusters of microcalcifications not found by the first reader, thereby improving the sensitivity of detecting clustered microcalcifications. Our laboratory has developed a computerized scheme for the detection of clustered microcalcifications, which can act like a second reader, that is undergoing clinical evaluation. This paper concerns the feature analysis stage of the computer scheme, which is designed to remove some of the false-computer detections. We have examined three methods of feature analysis, namely, rule based (the method currently used), an artificial neural network (ANN), and a combined method. In an independent database of 50 images, at a sensitivity of 83%, the average number of false positive (FP) detections per image was: 1.9 for rule-based, 1.6 for ANN, and 0.8 for the combined method. We demonstrate that the combined method performs best because each of the two stages eliminates different types of false positives.

The present study was conducted to assess the awareness and practice regarding early detection and prevention of breast and cervical cancers among women of kudumbashree units. A quantitative approach and non experimental descriptive design was used for the study. The study was theoretically supported by Nola J Pender's revised health promotion model. A total of 504 women of kudumbashree units were selected using multistage sampling technique. The data was collected using socio personal data sheet, structured questionnaire to measure the awareness and a rating scale to rate the practice regarding early detection and prevention of breast and cervical cancers, all developed by the researcher. The data was analysed using descriptive and inferential statistics. The findings of the study revealed that 81.9% of women of kudumbashree units had average awareness and 59.5% of women of kudumbashree units had good practice. There was no significant correlation between awareness and practice among women of kudumbashree units regarding early detection and prevention of breast and cervical cancers. The selected variables namely education of women of kudumbashree units, occupation of women of kudumbashree units and previous exposure to information regarding breast or cervical cancers had association with awareness regarding early detection and prevention of breast and cervical cancers. Also, there is no association between practice regarding early detection and prevention of breast and cervical cancers and any of the selected variables. An information booklet was prepared on early detection and prevention of breast and cervical cancers and distributed to all study participants.

Introduction: The use of social media by healthcare professionals to create disease awareness has been increased worldwide. It is easily available and accessible to the larger number of populations and hence helps in bridging the gap between doctor and patient. Breast cancer is the leading cause of cancer among women in India and the main reason for higher mortality among them is the late presentation to the surgeons, owing to various myths and social and physical taboos. We conducted a study to assess the awareness of breast cancer among the women in general population and also to check the effectiveness of WhatsApp in improving women's knowledge regarding the risk reduction, myths, early detection and treatment of breast cancer. The study was a pre-post health educational study including 146 wom Methods: en. Multiple WhatsApp groups were created to share information regarding breast cancer awareness, early detection, risk factors, common myths, self-breast examination and treatment of breast cancer in the form of audio, video, text and images. The group was active for 4-5 days as an interactive session. Data were collected through google forms. We noticed a signicant improvement in the knowledge of women regard Results: ing the common myths regarding breast cancer and the symptoms of breast cancer. There was signicant increase in performance of self-breast examination from 14.4% to 84.6% (p- &lt;0.001). Most of the women found the video of self-breast examination useful. The commonly Conclusion: used social networking site like WhatsApp can be used as a tool to assess the awareness of breast cancer among women. This also helped in exchange of the knowledge among women, which in turn helped in the risk reduction and early detection of breast cancer. This app is a useful tool that can be incorporated into health education strategies that focus on breast cancer.

Early detection of breast cancer is carried out by using mammographic images. Due to low contrast nature of these images, it is difficult to detect signs such as micro calcifications and masses. This paper describes novel algorithms for early detection of breast cancer using image processing techniques. Novel algorithms are implemented for 1) Mass region extraction to get exact shape of the mass 2) Superposition of boundary of mass on mammogram helps doctors to view the boundary easily as mass region overlaps with breast parenchyma 3) Extraction of texture features like mean, standard deviation, entropy, kurtosis etc, geometric features like area perimeter L:S, ENC, (Elliptical normalized circumference) wavelet based features, so that signatures can be assigned for identification and classification of benign and malignant masses. Fourteen patients' mammograms have been processed. Features of six patients have been extracted that have masses.

Reproductive health for housewives in the work area of the Passo Health Center is carried out based on problems from the partner group, namely low knowledge and minimal information about women's reproductive health, especially cervical cancer and breast cancer as well as skills in practicing or performing the BSE method resulting in low awareness of the importance women's reproductive health, this is shown through the low behavior of early detection carried out by housewives both the IVA test for early detection of cervical cancer and the application of the BSE method for early detection of breast cancer. With the aim of increasing the knowledge, skills and health of housewives in the target group regarding reproductive health, especially cervical cancer and breast cancer as a prevention effort. As well as increasing the knowledge of mothers about basic clinical information on cervical cancer and breast cancer, methods of detection as prevention and treatment methods, the number of mothers who are detected and can be treated. The solutions applied to the above problems are (1). Counseling to increase the knowledge of housewives about women's reproductive health (cervical Ca and breast Ca); (2). perform early detection of cervical cancer and early detection (screening) of breast cancer. The method used for the implementation of the solution is (1). Lectures, questions and answers and discussions both audio (sound) and visual (pictures and video) to increase mothers' knowledge about women's reproductive health; (2). Visual inspection method with acetic acid, also known as visual inspection with acetic acid (IVA test) for early detection of cervical cancer in housewives of the target group; Evaluation is carried out for each implementation of activities to encourage understanding of housewives to behave healthily towards reproductive health.

Background: Breast cancer is a major public health problem, with a high incidence among females worldwide, increasing with age and environmental conditions. Hormonal biomarkers have gained attention for their potential utility in the early detection of breast cancer. The study aimed to investigate the effect of biomarkers of different hormone levels, including prolactin, Testosterone, Cortisol, and Human Chorionic Gonadotropin (HCG) in the diagnosis and early detection of breast cancer risk. Methodology: Blood and saliva samples were collected from volunteer females, including 50 healthy females (NB), 111 benign breasts (BB), and 20 malignant breasts (MB) samples to determine hormone level biomarkers using enzyme-linked immunosorbent assay (ELISA). Results: The results showed that hormonal biomarkers, particularly prolactin, testosterone, and HCG, can serve as indicators for the early detection of breast cancer in both serum and saliva. In contrast, cortisol hormonal biomarkers did not have an effective association with early breast cancer detection. Conclusion: The results of this study showed that the present association between hormonal biomarkers, including prolactin, testosterone, and HCG, could serve as biomarkers for the early detection of breast cancer. In contrast, cortisol did not have an effective association with early breast cancer detection.

Fibroadenoma is the most benign breast neoplasm. These tumors grow in young women with the highest frequency in young women 21-25 years, with a prevalence of more than 9% of the female population affected by fibroadenoma. Breast cancer still has a high likelihood of being cured when it comes early or early. Early detection of breast cancer is an examination program to recognize breast cancer while still small, and before the cancer has a chance to spread. One of the early detection efforts is clinical breast examination (SADANIS) which is carried out at least once every 3 years or if abnormalities are found in the process of self breast examination (BSE) and carried out by trained health workers. The purpose of the study was to determine the relationship between knowledge and early detection of breast cancer risk in young women through SADANIS examination. Observational research method with cross sectional approach. The sample is a student of SMK 1 Palu and a student of AKBID Cendrawasih Palu. The results showed that there was a significant relationship between knowledge about SADANIS examination and the practice of SADANIS examination in young women. In line with the Wantini and Indrayani (2018) study that there is a meaningful relationship between knowledge with early detection of breast cancer through SADANIS examination based ontest Fisher Exact's with a p-value of 0.012&lt;α (0.05), so there is a strong relationship between young women's knowledge BSE in early detection of breast cancer as evidenced by the p-value 0.007&lt;α (0.05). The conclusion is that the knowledge of young women about sadanis examination is still low Keywords: Knowledge, Early Detection of Breast Cancer, Adolescent Girls, SADANIS

Cancer is a leading cause of death among men and women nowadays all over the world. Breast cancer is a most common form of cancer originated from breast tissue among women. Most frequent type of breast cancer is ductal carcinoma in situ (DCIS) and most frequent symptoms of DCIS recognized by mammography are clusters of Microcalcifications (MCCs). Automatic detection of Microcalcifications is an important task to prevent and treat the disease. In this paper, an effective approach for automatic detection of Microcalcifications in digitized mammograms is proposed. The proposed approach is based on fuzzy 2-partition entropy and mathematical morphology. In the proposed approach, first phase uses fuzzy Gaussian membership function for mammogram fuzzification. In this phase, fuzzy 2-partition entropy approach is used to find bandwidth of the Gaussian function. After this, mathematical morphological enhancement approach is used to enhance the contrast of Microcalcifications in mammograms. Finally, Microcalcifications are located using Otsu threshold selection method. Experiments have been conducted on images of mini-MIAS database (Mammogram Image Analysis Society database (UK)). In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and denseglandular) of mini-MIAS database are considered. Experimental results demonstrate that the proposed approach has an ability to detect Microcalcifications even in dense mammograms. The results of proposed approach are quite promising. The proposed approach can be a part of developing a computer aided decision (CAD) system for early detection of breast cancer.

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.

Breast cancer remains one of the major global health challenges, the Human Epidermal Receptor-2 (HER-2) subtype is characterized by aggressive tumor growth due to overexpression of the HER2-positive. Early detection of HER2-positive breast cancer is crucial for improving patient’s outcomes and reducing the risk of metastatic disease. Circulating Tumor DNA is DNA fragments which are released by cancer cells into the bloodstream, and has emerged as a promising non-invasive biomarker for early cancer detection. This review will explore the role of ctDNA in the early detection of HER2-positive breast cancer and its role in prognosis. Studies have shown the high relevance of ctDNA as a diagnostic biomarker, particularly in early-stage disease. Multiple studies have evaluated the role of ctDNA in breast cancer patients, which results consistently showed a strong relation between ctDNA detection and early-stage disease. Additionally, research has indicated that high ctDNA levels are related with a higher risk of recurrence and poorer survival outcomes. The non-invasive nature of ctDNA testing offers several advantages compared to traditional methods, including the ability of monitoring disease progression and treatment response without any need for invasive procedures. Early detection of HER2-positive breast cancer through ctDNA analysis can potentially improve patient outcomes by enabling timely intervention and targeted therapies. By identifying patients with high risk and monitoring disease status, ctDNA testing may contribute to more effective management of HER2-positive breast cancer. While ctDNA analysis shows great promise, it is also important to acknowledge the current limitations and challenges associated with its clinical application. Issues such as standardization of detection methods, sensitivity, and specificity need to be addressed to ensure the widespread adoption of ctDNA testing in routine clinical practice. In conclusion, ctDNA represents a promising biomarker for the early detection and management of HER2-positive breast cancer. Further research and advancements in ctDNA analysis are essential to completely realize its potential and improve patient outcomes.