Predicting and Classifying Breast Cancer Using Machine Learning

The computer-assisted classification of breast cancer histopathological image in the future is an essential method for the improvement of the diagnostic performance, thus reducing breast cancer deaths. Although deep convolutional neural networks (DCNNs) have made dramatic breakthroughs, many image classification tasks still remain challenging due to the insufficiency of training data and the lack of the ability to focus on improving classification efficiency. To address these issues, we share a densely connected convolutional network (DenseNet) model that is composed of 3 layers DenseNet, pooling layer, and a classification layer for breast cancer classification in microscopic images. Each layer of DenseNet contains 4 dense blocks. Each dense block jointly uses the dense connection and a novel attention learning mechanisms to increase its ability for discriminative representation. Meanwhile, the transfer learning algorithm is applied to determine the model parameters to extract the features of the patient image that is performed. In order to ensure sufficient data volume, a data enhancement method based on the quad-tree principle is proposed for high-resolution images. On the other hand, the classification probability of each part after dicing is fused by three algorithms of addition, product, and maximum. We evaluated our DenseNet model on the BreastKHis dataset. Our results indicate that the DenseNet model and data enhancement method we adopted can adaptively focus on the study of breast cancer histopathological image classification, thus achieving the state-of-the-art performance in breast cancer classification. The results of the experiments are in terms of patient-level and image-level accuracy. The best recognition accuracy increased to 90.9%-92.5% and 89.3%-91.8%, respectively, compared with previous studies.

Although breast cancer, with easy recurrence and high mortality, has become one of the leading causes of cancer death in women, early and accurate diagnosis of breast cancer can effectively increase the likelihood of a cure. Therefore, it is particularly important to improve the accuracy of early diagnosis of breast cancer. However, conventional early diagnosis relies on human experience and has a low accuracy rate. Therefore, many researchers have proposed various machine learning methods to improve the accuracy and efficiency of prediction. Most of the existing studies around breast cancer classification adopt a single algorithm to fit breast cancer data but ignore the applicability of different breast cancer data features to the model. In this paper, we adopt machine algorithms to strip the features of machine learning methods from the rest of the features and attempt to enhance the model effect by designing deep learning model structures to find the hidden patterns in the rest of the features. In addition, due to strict medical data privacy requirements and high collection difficulty and cost, the model designed in this paper will be trained on a small number of samples. As a result, we attempt to find a minimization model for a breast cancer classification algorithm that features both low cost and high efficiency. At the same time, the deep learning model is further designed to complement the original model when it is possible to introduce complex data indicators. Experimental values show that the design model in this paper performs best not only under limited data and limited indicators but also under limited data complex indicators, demonstrating the effectiveness of the approach of mixed comparison and feature selection of multiple classification algorithms. In summary, the fusion model designed and implemented in this paper performs well in the experiments, and the accuracy of the model test reaches 98.3%.

A time for creative collaboration

Cancer is the most deadly disease by nature. Breast cancer is the top cancer in women globally and led to crucial increase in cancer fatality. Initial stage cancers do not cause death in human beings. Excessive work is carried out for the early diagnosis so that patients can get successful treatment. Even though health authorities recommend regular screening with mammography in women, infrared thermography can be a potential adjunct mechanism for identifying breast cancer. Deep learning techniques have opened up a new age in the image classification especially in the computer aided medical image analysis. A novel attempt combining deep learning and thermography for early stage breast cancer diagnosis is proposed in this paper. A deep learning scheme is adopted for the classification of cancerous and normal thermal rat images based on the transfer learning technique. For extracting features multiple deep pre-trained networks are utilized and classification is done with a support vector machine. The proposed method is also compared with a standard softmax classifier.

Rationale and ObjectivesThe human epidermal growth factor receptor 2 (HER2) gene status is crucial for determining treatment efficacy. This study assessed preoperative HER2 classification in breast cancer using machine learning based on clinicopathological and MRI characteristics.Materials and MethodsThis retrospective study involved 1,015 patients (1,030 lesions) across two centers. Patients were divided into training, internal validation, and external validation sets. Nomograms were developed using clinicopathological and MRI features. Predictive models were constructed using decision trees (DT), support vector machines (SVM), k-nearest neighbors (k-NN), artificial neural networks (ANN), and multivariable logistic regression (LR). Model performance was evaluated using receiver operating characteristic curves, decision curve analysis, and calibration curves. Model interpretability was achieved by developing nomograms and employing SHAP (SHapley Additive exPlanations) analysis.ResultsKey variables for distinguishing HER2-positive from HER2-negative cases included regional N category, estrogen receptor, PR (progesterone receptor) status, Ki-67 status, lesion number, distribution quadrant, and accompanying signs. The SVM model achieved the highest AUC of 0.86 (95% confidence interval (CI): 0.81–0.90) in the training set, while the ANN model had an AUC of 0.77 (95% CI: 0.67–0.86) in the internal validation set. In the external validation set, the LR model achieved the highest AUC of 0.66 (95% CI: 0.56–0.76), although the overall performance was modest. For HER2-low versus HER2-zero differentiation, Ki-67 status, lesion number, distribution quadrant, mass shape, early enhancement rate, and ADC (apparent diffusion coefficient) were significant. The SVM model attained the highest AUC of 0.87 (95% CI: 0.83–0.91) in the training set, while the LR model demonstrated superior generalizability, yielding the highest AUCs in both the internal and external validation sets (internal: 0.67, 95% CI: 0.58–0.76; external: 0.74, 95% CI: 0.65–0.83). Radiologists benefited from the nomogram for improved diagnostic accuracy, especially junior radiologists. SHAP analysis revealed that PR status was paramount for HER2-positive classification, whereas mass shape and ADC values were dominant for identifying HER2-low status.ConclusionIntegrating machine learning with clinicopathological and MRI characteristics improves the accuracy of HER2 status classification in breast cancer and enhances diagnostic capabilities for radiologists in clinical practice.

The most severe disease nowadays is breast cancer. The abnormal development of breast cells is what causes breast cancer. The breast ducts may include this type of malignancy. Women over 40 are the ones who get diagnosed with breast cancer most frequently. Women over 50 have the highest incidence (about 77% of invasive cases). A clear understanding of breast cancer and its kinds, as well as the creation of prophylactic measures, have made significant strides. Many genes have been linked to breast cancer. Malignant and benign cells both fall within this category. Cancerous malignant conditions exist. Non-cancerous benign cancer. Malignant cells affect other cells of the body as well as the tissues continue to divide uncontrollably. While benign tumors are significantly less dangerous than malignant ones since they do not spread to other parts of the body. Treatment is not necessary for these cancers. Malignant tumors can be treated with chemotherapy, radiation therapy, and immunotherapy, among other forms of treatment. The algorithms used to develop this prediction system are machine learning-based. Machine learning became a research focus and was found to be an effective method. Recently, many scholars have become interested in the developing technique of machine learning. Scikit-learn is used to create a model that categorizes breast cancer as either malignant or benign. A type of library that is included with the collab platform is called Scikit-learn. Mammograms have historically been the primary method of finding breast cancer. Any calcium deposits or calcifications in the breasts might be seen on a mammogram. Considering how pricey it is, few people can afford it. It will be great if an early diagnosis occurs so that treatment can get underway right away. Many women's lives could be saved by this. This dataset analysis will benefit from the use of machine learning algorithms. Six machine learning (ML) methods are compared in this paper: Naive Bayes (NB), Random Forest (RT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT). These methods will be applied to accurately forecast outcomes. Due to early detection, the 5-year survival rate for breast cancer patients is above 82 %. The eventual goal of this project is to assist clinicians in analyzing enormous databases of available cancer data and discovering patterns using patient data and that cancer data. This endeavor to a modest stride in the protracted battle against breast cancer.

MF-OMKT: Model fusion based on online mutual knowledge transfer for breast cancer histopathological image classification

Optimization and normalization strategies for long term untargeted HILIC-LC-qTOF-MS based metabolomics analysis: Early diagnosis of breast cancer

ความเป็นมา การตอบสนองทางพยาธิวิทยาภายหลังการได้รับยาเคมีบำบัดก่อนผ่าตัดมีความสัมพันธ์กับผลลัพธ์ทางคลินิก ปัจจุบันไม่มีปัจจัยใดที่สามารถทำนายการตอบสนองต่อยาเคมีบำบัดในมะเร็งเต้านมชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูได้อย่างแม่นยำ เช่นเดียวกับ Ki-67 การแสดงออกของโปรตีน MCM2 ซึ่งมีหน้าที่ควบคุมการสังเคราะห์ดีเอ็นเอในระดับสูงบ่งชี้ถึงการแบ่งตัวของเซลล์ที่รวดเร็ว และมีความสัมพันธ์กับการพยากรณ์โรคที่ไม่ดีในมะเร็งหลายชนิดรวมถึงมะเร็งเต้านม การศึกษานี้มีวัตถุประสงค์เพื่อศึกษาการแสดงออกของ MCM2 ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดเพื่อทำนายการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ในมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู วิธีการศึกษา ทบทวนเวชระเบียนอิเล็กทรอนิกส์ของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทูที่ได้รับยาเคมีบำบัดก่อนผ่าตัดในโรงพยาบาลจุฬาลงกรณ์ ระหว่าง 1 มกราคม 2558 - 31 ธันวาคม 2563 นำชิ้นเนื้อก่อนได้รับยาเคมีบำบัดตรวจการแสดงออกของ MCM2 ด้วยการย้อมอิมมูโนฮิสโตเคมีและทบทวนลักณะทางพยาธิวิทยาเพื่อคำนวน RCB index ในชิ้นเนื้อหลังได้รับยาเคมีบำบัด คำนวนการติดสีของ MCM2 ด้วยโปรแกรมวิเคราะห์ภาพ Aperio Imagescope ผลการวิจัย ผู้ป่วยทั้งหมด 88 คน มีค่ามัธยฐานอายุ 49 ปี (พิสัย 27-80 ปี) 61.4% เป็นวัยก่อนหมดประจำเดือน มีระยะโรคมะเร็งทางคลินิก cT4 และ cN1 40% และ 66.7% ตามลำดับ เป็นมะเร็งเต้านมชนิด invasive ductal carcinoma 91.1% มีระดับทางพยาธิวิทยาระดับที่ 2 58.9% มีการแสดงออกของ ER >10% 92.2% และสถานะ PR เป็นลบ 22.2% ในชิ้นเนื้อมะเร็งเต้านมทั้งหมด 72 ชิ้นที่สามารถย้อม MCM2 และประเมิน RCB index ได้ พบว่าการแสดงออกของ MCM2 >=40% มีความสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I อย่างมีนัยสำคัญทางสถิติจากการวิเคราะห์แบบ univariate analysis (OR = 18.33 ช่วงความเชื่อมั่น 95% = 1.88-178.98 p-value = 0.012) ปัจจัยทางพยาธิวิทยาคลินิกที่พบว่ามีความสัมพันธ์กับ MCM2 >=40% คือ ระดับของพยาธิวิทยาระดับที่ 3 การแสดงออกของ ER <=10% สถานะ PR เป็นลบ และ Ki-67 >=20% แต่ในการวิเคราะห์แบบ multivariate analysis ไม่มีปัจจัยทางพยาธิวิทยาคลินิกใดสัมพันธ์กับการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I ลักษณะทางอิมมูโนฮิสโตเคมีก่อนและหลังให้ยาเคมีบำบัดมีลักษณะโดยรวมใกล้เคียงกัน ค่ามัธยฐานของการปลอดโรค และอัตราการปลอดโรคที่ 3 ปี ไม่มีความแตกต่างกันอย่างมีนัยสำคัญทางสถิติระหว่างผู้ป่วยที่มีการตอบสนองทางพยาธิวิทยาแบบ RCB 0/I และ RCB II/III สรุปผลการวิจัย การแสดงออกของ MCM2 >=40% ด้วยการตรวจทางอิมมูโนฮิสโตเคมี ในชิ้นเนื้อก่อนได้รับยาเคมีบำบัดของผู้ป่วยมะเร็งเต้านมระยะต้นชนิดมีตัวรับฮอร์โมนและไม่มีตัวรับเฮอร์ทู มีความสัมพันธ์กับการตอบสนองแบบ RCB 0/I การตรวจการแสดงออกของ MCM2 อาจช่วยทำนายประโยชน์ของยาเคมีบำบัดก่อนผ่าตัดในผู้ป่วยกลุ่มนี้ได้ อย่างไรก็ตามควรมีการศึกษาเพิ่มเติมในประชากรที่มีขนาดใหญ่ขึ้นเพื่อยืนยันผลของการศึกษานี้

Breast cancer is one of the deadliest diseases, especially for women. Early diagnosis of breast lesions and differentiation of malignant nodules from benign nodules and normal nodules are important for breast cancer prognosis. In diagnosing this disease, one radiological method, namely medical image analysis using ultrasonography, can be used to determine early diagnosis of breast cancer. Breast cancer ultrasound images have several characteristics, such as color, shape, size, and texture, which make segmentation difficult due to object accumulation. This study implements a Convolutional Neural Network classification algorithm and modified watershed segmentation to separate nodules or tumors in breast cancer. From the segmentation performance test with Watershed Transform, the average ZSI index was 40% for malignant images and 60% for benign images. The results of the VGG architecture for classification modeling showed 47% for watershed segmentation and 80% without watershed segmentation.

Navigating the complex landscape of breast cancer diagnostics, this study addresses the critical challenge of early detection and classification. Breast cancer remains a significant health concern, underscoring the urgency for advanced method-ologies. Employing various machine learning classifiers kMPN (k-Most Proximate Neighbor), Naive Bayes, and the formidable Support Vector Machine (SVM)-we rigorously assessed their capabilities in discriminating between benign and malignant tumors. In a groundbreaking revelation, SVM emerged as the indisputable leader, achieving an exceptional accuracy of 93.72%. This milestone not only represents a pivotal advancement in breast cancer diagnostics but also underscores the indispensable role of machine learning in healthcare. As we grapple with the intricacies of breast cancer, SVM's outstanding accuracy positions it as the optimal choice for refining and advancing detection methodologies, offering a beacon of hope for improved patient outcomes and transformative progress in addressing this pervasive health issue.

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.

Early detection and diagnosis of breast cancer are critical for saving lives. This paper addresses two major challenges associated with this task: the vast amount of data processing involved and the need for early detection of breast cancer. To tackle these issues, we developed thirty hybrid architectures by combining five deep learning techniques (Xception, Inception-V3, ResNet50, VGG16, VGG19) as feature extractors and six classifiers (random forest, logistic regression, naive Bayes, gradient-boosted tree, decision tree, and support vector machine) implemented on the Spark framework. We evaluated the performance of these architectures using four classification criteria. The results, analyzed using Scott Knott's statistical test, demonstrated the effectiveness of merging deep learning feature extraction techniques with traditional classifiers for classifying breast cancer into malignant and benign tumors. Notably, the hybrid architecture using logistic regression as the classifier and ResNet50 for feature extraction (RESLR) emerged as the top performer. It achieved impressive accuracy scores of 98.20%, 96.59%, 96.64%, and 94.84% across the Break-His dataset at different magnifications (40X, 100X, 200X, and 400X) respectively. Additionally, RESLR achieved an accuracy of 97.05% on the ICIAR dataset and a remarkable accuracy of 95.31% on the FNAC dataset.

Breast cancer is the primary malignant tumor that endangers women's health. The incidence of breast cancer is increasing rapidly in recent years. Accurate disease evaluation before treatment is the key to the selection of treatment options. Biomedical imaging technology plays an irreplaceable role in the diagnosis and staging of tumors. Various imaging methods can provide excellent temporal and spatial resolution from multiple levels and perspectives and have become one of the most commonly used means of breast cancer early detection. With the development of radiomics, it has been found that early imaging diagnosis of breast cancer plays an important guiding role in clinical decision-making. The purpose of this study is to explore the characteristics of various breast cancer imaging technologies, promote the development of individualized accurate diagnosis and treatment of imaging, and improve the clinical application value of radiomics in the early diagnosis of breast cancer.

Background: The role of HER2 expression in HER2 negative breast cancer is evolving. Whether HER2 expression has an impact on ER positive, HER2 negative early-stage breast cancer in unclear. Oncotype DX assay Recurrence Score comprises of standardized quantitative HER2 mRNA expression levels by RT-PCR. The objective of this study was to investigate the impact of Oncotype HER2 score on tumor characteristics and outcome. Methods: All women assigned to the Clalit Health Services registry between 2006 to 2011 with ER positive, HER2 negative early-stage breast cancer whose tumor were sent for oncotype were identified. Patients in which HER2 score was reported in the oncotype results were included. Differences in age, tumor characteristics and chemotherapy administration between lower HER2 score (H1), i.e. HER2 score ≤ 9.1, to higher HER2 score (H2), i.e. 9.2-10.7, were analyzed. 5-year Kaplan–Meier for distant recurrence free survival (DRFS) and overall survival (OS) were estimated and differences between H1 and H2 were evaluated. Analyses were repeated separately for node negative and for node positive (N mic and 1-3 lymph nodes) disease. Results: A total of 1535 patients were included, 948 patients with node negative disease and 587 with node positive disease. Of these, 814 and 721 patients were categorized to the H1 and H2 groups, respectively. Median follow-up was 5.4 years. The distribution of oncotype RS was different, with significantly lower proportion of patients with RS between 0-10 and higher proportion of RS above 25 in the H1 group compared to the H2 groups. This difference was identified both for patients with node negative and node positive disease (Table). The distributions of age, tumor size and histological subtype were comparable between the H1 and the H2 groups, both for node negative and node positive disease. Within each RS group, administration of chemotherapy was comparable between H1 and H2 groups, regardless of nodal involvement. DRFS and OS were not associated with HER2 score. For RS 0-25, node negative disease, 5-year DRFS was 96.2% in H1 and 96.4% in H2 groups (p=0.92) and 5-year OS was 97.2% in H1 and 98.1% in H2 groups (p=0.39). For RS >25, node negative disease, 5-years DRFS was 89% in H1 and 90.8% in H2 groups (p=0.33) and 5-year OS was 90.6% in H1 and 90.8% in H2 groups (p=0.7). Similarly, for node positive disease, DRFS and OS were comparable between H1 and H2 group in all subgroups. Conclusions: Low HER2 mRNA expression by Oncotype RT-PCR assay was associated with significantly high Oncotype RS. Patients' outcome was not directly affected by HER2 score and was determined by RS. Further study is desired to clarify the role of HER2 expression in early stage, luminal breast cancer. Table. Comparison of age and tumor characteristics between low (H1) and high (H2) HER2 score Citation Format: Hadar Goldvaser, Raz Mutai, Salomon Stemmer, Iryna Kuchuk, Margarita Tokar, Shani Paluch-Shimon, Rinat Yerushalmi, Karen Drumea, Ella Evron, Amir Sonnenblick, Einav Gal-Yam, Gil Bar Sela, Ayelet Shai, Rotem Merose, Avital Bareket-Samish, Lior Soussan-Gutman. The impact of Human Epidermal growth factor Receptor 2 (HER2) expression by RT-PCR on tumor characteristics and outcome in estrogen receptor (ER) positive, HER2 negative early-stage breast cancer [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 PO5-01-08.