Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

Background: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296–2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497–510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. Objective: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. Methods: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. Results: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. Conclusion: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.</p

Breast Cancer Detection by Extracting and Selecting Features Using Machine Learning

The cancer of the breast is a significant cause of female death worldwide, but especially in developing countries. For better results and higher survival rates, early diagnosis and screening are crucial. Machine learning (ML) methods can aid in the initialdiscovery and diagnosis of breast cancer by choosing the most informative elements from medical data and eliminating irrelevant ones. The approach of feature extraction involves taking unstructured data and extracting a representative set of characteristics that may be used to classify or forecast data. The aim is to decrease the dimensionality of the feature space while upholding or even refining the accuracy of the ML model. An artificial intelligence model is developed on the given features to categorize mammography images into benign and malignant groups. Different supervised learning techniques, including support vector machines, random forests, and artificial neural networks, are employed and contrasted in order to select the best-performing model. This research offers a comprehensive framework for utilizing machine learning methods to detect breast cancer. The technique demonstrates how it might assist radiologists in the early detection of breast cancer by effectively extracting and selecting critical characteristics that could improve patient outcomes and potentially save lives

Machine Learning-Based Hybrid Recommendation (SVOF-KNN) Model For Breast Cancer Coimbra Dataset Diagnosis

An effective way to identify breast cancer is by creating a prediction algorithm using risk factors. Models for ML have been used to improve the effectiveness of early detection. This article analyses a KNN combined with singular value decomposition and Grey wolf optimization(GWO) method to give a detection of breast cancer(BC) at the early phase depending on risk metrics. The SVD technique was utilized to eliminate the reliable feature vectors, the GW optimizer was used to select the feature vectors, and while KNN model was used to diagnose the BC status. The proposed hybrid recommendation model (SVOF-KNN) for BC prediction's main objective is to give an accurate recommendation for BC prognosis through four different steps such as;BCCD dataset collection, data pre-processing, feature selection, and classification/recommendation. It is implemented to classify the consequence of risk metrics connected withregular blood analysis(BA) in the BCCD database. The aspects of the BC dataset are insulin, glucose, HOMA, Leptin, resistin, etc. The error categories such as RMSE and MAE are used to calculate the exception values for each instance of the BC dataset. It hybrid model has recommended the best score instance having the minimumexception rateas the defined features for BC prediction. It improves significance in automatic BC classification with the optimum solution. The hybrid recommendation model (SVOF-KNN) also recommends the accurateclassification method for BC diagnosis. The results of this work shall enhance the QoS in BC care

Domain Generalization in Biosignal Classification

Objective: When training machine learning models, we often assume that the training data and evaluation data are sampled from the same distribution. However, this assumption is violated when the model is evaluated on another unseen but similar database, even if that database contains the same classes. This problem is caused by domain-shift and can be solved using two approaches: domain adaptation and domain generalization. Simply, domain adaptation methods can access data from unseen domains during training; whereas in domain generalization, the unseen data is not available during training. Hence, domain generalization concerns models that perform well on inaccessible, domain-shifted data. Method: Our proposed domain generalization method represents an unseen domain using a set of known basis domains, afterwhich we classify the unseen domain using classifier fusion. To demonstrate our system, we employ a collection of heart sound databases that contain normal and abnormal sounds (classes). Results: Our proposed classifier fusion method achieves accuracy gains of up to 16% for four completely unseen domains. Conclusion: Recognizing the complexity induced by the inherent temporal nature of biosignal data, the two-stage method proposed in this study is able to effectively simplify the whole process of domain generalization while demonstrating good results on unseen domains and the adopted basis domains. Significance: To our best knowledge, this is the first study that investigates domain generalization for biosignal data. Our proposed learning strategy can be used to effectively learn domain-relevant features while being aware of the class differences in the data

Diagnostic Significance of Exosomal miRNAs in the Plasma of Breast Cancer Patients

Poster Session AbstractsBackground and Aims: Emerging evidence that microRNAs (miRNAs) play an important role in cancer development has opened up new opportunities for cancer diagnosis. Recent studies demonstrated that released exosomes which contain a subset of both cellular mRNA and miRNA could be a useful source of biomarkers for cancer detection. Here, we aim to develop a novel biomarker for breast cancer diagnosis using exosomal miRNAs in plasma. Methods: We have developed a rapid and novel isolation protocol to enrich tumor-associated exosomes from plasma samples by capturing tumor specific surface markers containing exosomes. After enrichment, we performed miRNA profiling on four sample sets; (1) Ep-CAM marker enriched plasma exosomes of breast cancer patients; (2) breast tumors of the same patients; (3) adjacent non-cancerous tissues of the same patients; (4) Ep-CAM marker enriched plasma exosomes of normal control subjects. Profiling is performed using PCR-based array with human microRNA panels that contain more than 700 miRNAs. Results: Our profiling data showed that 15 miRNAs are concordantly up-regulated and 13 miRNAs are concordantly down-regulated in both plasma exosomes and corresponding tumors. These account for 25% (up-regulation) and 15% (down-regulation) of all miRNAs detectable in plasma exosomes. Our findings demonstrate that miRNA profile in EpCAM-enriched plasma exosomes from breast cancer patients exhibit certain similar pattern to that in the corresponding tumors. Based on our profiling results, plasma signatures that differentiated breast cancer from control are generated and some of the well-known breast cancer related miRNAs such as miR-10b, miR-21, miR-155 and miR-145 are included in our panel list. The putative miRNA biomarkers are validated on plasma samples from an independent cohort from more than 100 cancer patients. Further validation of the selected markers is likely to offer an accurate, noninvasive and specific diagnostic assay for breast cancer. Conclusions: These results suggest that exosomal miRNAs in plasma may be a novel biomarker for breast cancer diagnosis.link_to_OA_fulltex

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

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Molecular Imaging

The present book gives an exceptional overview of molecular imaging. Practical approach represents the red thread through the whole book, covering at the same time detailed background information that goes very deep into molecular as well as cellular level. Ideas how molecular imaging will develop in the near future present a special delicacy. This should be of special interest as the contributors are members of leading research groups from all over the world

Cancer of the Uterine Endometrium

The book Cancer of the Uterine Endometrium - Advances and Controversies brings together an international collaboration of authors who share their contributions for the management of endometrial carcinoma. The scope of the text is not basic, but rather aims to provide a comprehensive and updated source of advances in the diagnosis and therapeutic strategies in this field of gynecologic cancer. Each section in the book attempts to provide the most relevant evidence-based information in the biology and genetics, modern imaging, surgery and staging, and therapies for endometrial cancer. It is hoped that future editions will bring additional authors to contribute to this endeavor. To this end, it is our patients who will benefit from this work