scholarly journals A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans

2020 ◽  
Vol 10 (3) ◽  
pp. 997 ◽  
Author(s):  
Nikolaos Papandrianos ◽  
Elpiniki Papageorgiou ◽  
Athanasios Anagnostis ◽  
Anna Feleki
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Classification Accuracy ◽  
Medical Image Analysis ◽  
Whole Body ◽  
Imaging Method ◽  
Learning Approach ◽  
Breast Cancer Patients

(1) Background: Bone metastasis is one of the most frequent diseases in breast, lung and prostate cancer; bone scintigraphy is the primary imaging method of screening that offers the highest sensitivity (95%) regarding metastases. To address the considerable problem of bone metastasis diagnosis, focused on breast cancer patients, artificial intelligence methods devoted to deep-learning algorithms for medical image analysis are investigated in this research work; (2) Methods: Deep learning is a powerful algorithm for automatic classification and diagnosis of medical images whereas its implementation is achieved by the use of convolutional neural networks (CNNs). The purpose of this study is to build a robust CNN model that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer; (3) Results: A robust CNN architecture is selected based on CNN exploration performance for bone metastasis diagnosis using whole-body scan images, achieving a high classification accuracy of 92.50%. The best-performing CNN method is compared with other popular and well-known CNN architectures for medical imaging like ResNet50, VGG16, MobileNet, and DenseNet, reported in the literature, providing superior classification accuracy; and (4) Conclusions: Prediction results show the efficacy of the proposed deep learning approach in bone metastasis diagnosis for breast cancer patients in nuclear medicine.

scholarly journals Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 518
Author(s):  
Da-Chuan Cheng ◽  
Te-Chun Hsieh ◽  
Kuo-Yang Yen ◽  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Prostate Cancer ◽  
Deep Learning ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Bone Scintigraphy ◽  
Breast Cancer Patients ◽  
Location Detection ◽  
The Mean ◽  
Mean Sensitivity

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians’ final decisions.

scholarly journals Radiomics Methods to Differentiate Metastasis and Primary Lung Cancer of Breast Cancer Patients in PET/CT

2021 ◽  
Author(s):  
Xinglu Zhou ◽  
Yunsong Peng ◽  
Yingci Li ◽  
Jiarui Zhang ◽  
Tianyi Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Lung Cancer ◽  
Cancer Patients ◽  
Roc Curve ◽  
Primary Lung Cancer ◽  
Whole Body ◽  
Imaging Method ◽  
Pathological Features ◽  
Breast Cancer Patients ◽  
Pet Ct

Abstract Purpose：Positron emission tomography (PET) with integrated computed tomography (PET/CT) is a whole-body imaging method providing information the entire body. When it was used in staging breast cancer patients, quite a few patients were found to have a second primary lung cancer(PLC), which was has few distinguishing features from breast cancer metastasis(MBC). Therefore, based on CT, LDCT and PET images, combined with pathological features, we established radiomics models to distinguish between MBC and PLC.Methods：We retrospectively collected CT, LDCT, and PET images, and pathology features of 100 breast cancer patients, including 60 metastases of breast cancer(MBC) and 40 primary lung cancers(PLC). The two radiologists manually drew a region of interest around the whole visible tumor in consensus. Python 3.8 and Pyradiomics toolkit are used to extract features from CT, LDCT, and PET. The linear discriminant analysis (LDA) classifier was used to build the radiomics model. The receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) were used to evaluate the classification performance.Results：Total 12, 13, and 9 features were selected from the CT, LDCT, and PET respectively. The model based on the LDCT and PET obtained the same highest AUC (0.9479). The combination with CT and pathology features showed a highest AUC of 0.9583 with a sensitivity of 1.000 and a specificity of 0.8333. Conclusion：Overall, the results are encouraging that radiomics models based on CT, LDCT and PET can differentiate between MBC and PLC pathological features could significantly improve the AUC and ACC of CT model.

Deep Learning for Bone Metastasis Localisation in Nuclear Imaging data of Breast Cancer Patients

2021 ◽  
Author(s):  
Serafeim Moustakidis ◽  
Athanasios Siouras ◽  
Nikolaos Papandrianos ◽  
Charis Ntakolia ◽  
Elpiniki Papageorgiou
Keyword(s):  
Breast Cancer ◽  
Deep Learning ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Nuclear Imaging ◽  
Imaging Data ◽  
Breast Cancer Patients

COX-2 expression in bone metastasis of breast cancer patients

2007 ◽  
Vol 4 (02) ◽  
Author(s):  
LC Horn ◽  
A Meinel ◽  
C Pleul ◽  
C Leo ◽  
P Wuttke
Keyword(s):  
Breast Cancer ◽  
Bone Metastasis ◽  
Cancer Patients ◽  
Breast Cancer Patients ◽  
Cox 2

scholarly journals A machine learning approach to predict healthcare cost of breast cancer patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pratyusha Rakshit ◽  
Onintze Zaballa ◽  
Aritz Pérez ◽  
Elisa Gómez-Inhiesto ◽  
Maria T. Acaiturri-Ayesta ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Healthcare Cost ◽  
Percentage Error ◽  
Learning Approach ◽  
Early Prediction ◽  
Breast Cancer Patients ◽  
Machine Learning Approach ◽  
Clinical Records

AbstractThis paper presents a novel machine learning approach to perform an early prediction of the healthcare cost of breast cancer patients. The learning phase of our prediction method considers the following two steps: (1) in the first step, the patients are clustered taking into account the sequences of actions undergoing similar clinical activities and ensuring similar healthcare costs, and (2) a Markov chain is then learned for each group to describe the action-sequences of the patients in the cluster. A two step procedure is undertaken in the prediction phase: (1) first, the healthcare cost of a new patient’s treatment is estimated based on the average healthcare cost of its k-nearest neighbors in each group, and (2) finally, an aggregate measure of the healthcare cost estimated by each group is used as the final predicted cost. Experiments undertaken reveal a mean absolute percentage error as small as 6%, even when half of the clinical records of a patient is available, substantiating the early prediction capability of the proposed method. Comparative analysis substantiates the superiority of the proposed algorithm over the state-of-the-art techniques.

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