Deem, Ryan

Meador, Cora L

English

DigitalCommons@Kennesaw State University

BreastNet: Breast Cancer Survival Prediction With Deep LearningAbstractAuthors: Ryan Deem and Cora MeadorAdvisor: Dr. Mohammed AledhariUR-124In the United States, 12% of women are diagnosed with breast cancer in theirlifetime, and it is the second leading cause of cancer-related death in women.Early detection and screening can improve the patient's life expectancy of 10years on average. Unfortunately, breast cancer can be challenging to detect,since it can appear anywhere in the breast tissue. Detecting the cancer before ithas progressed into aggressive stages can give patients more options and savethousands of dollars in medical costs. Magnetic Resonance Imaging (MRI)produces high quality images that can be used by Radiologists as a diagnostictool for breast cancer. Additionally, advances in Machine Learning technologyenable computers to learn from high-quality labeled datasets to achieve human-like ability in well-defined problem area.ConclusionsResultsIntroductionThe ResNet CNN accuracy score of about 98% between our training, testing,and validation sets. After training it for 100 epochs we got an AUC score of0.9986. This is the AUC for when positive is when someone survives within the 5years. Then we also had an F1 score of 0.9980.BreastNet is a model that contributes to both the medical field and data science.In particular, some of the data preprocessing work can be valuable to other datascientists who work on similar problems in biostatistics. The inclusion of aprogram that is able to convert a DICOM format, which is highly isolated andmostly used by the medical community alone, into the numpy format, can makeit easier for people who primarily manipulate python-native data formats.Bycreating a network that directly manipulates 3D numpy arrays from directories,BreastNet paves the way for general purpose computational intelligence to beused to model 3 dimensional image data in a simple, straightforward wayBreastNet contributes to a growing body of studies that use machine learning tohelp cancer patients understand their diagnosis and make informed treatmentdecisions \cite{review}. By understanding whether they have a high or low risk ofdeath within the next five years, they will have agency to maximize their qualityof life. By focusing on specific results that patients find most important whenlearning about their breast cancer diagnosis, BreastNet can help make apositive impact on people’s lives; one that goes beyond the simple results theprogram generates.We would like to thank our faculty advisor Dr. Mohammed Aledhari, the KSUCollege of Computer Science and Software Engineering, and the Office ofUndergraduate Research for supporting our workRyan Deem’s LinkedIn: https://www.linkedin.com/in/rtdeem/Ryan Deem’s email: rdeem@students.kennesaw.eduCora Meador LinkedIn: https://www.linkedin.com/in/cora-meador/Cora Meador email: cmeador6@students.kennesaw.eduResearch Question(s)AcknowledgmentsContact InformationReferencesThe main goal of BreastNet is to predict the 5-year survival rate of breastcancer patients. We use a Convolutional Neural Network (CNN) to predictsurvival based on pre-processed MRI data, and a Recurrent Neural Network(RNN) to predict survival based clinical data. By implementing dual modelsbased upon different data sources, we can create a robust classification systemthat makes data-driven decisions based upon the most information possible.We trained the CNN using 12254 sagittal slices from 340 MRI volumes for 222patients, and we trained the RNN using a language-vector model withvocabulary tokenization. Additionally, we employ methods such as stochasticresampling to guard against class imbalance bias. We apply a disease-specificsurvival censor to enable the models to use binary classification for each inputtensor. Since breast cancer is a disease that drastically affects people’s lives,there is a great need for technology that can help patients make informeddecisions about the treatment plan they wish to pursue.We used MRI images and Clinical Data from the ACRIN 6657 Clinical Trial,which is publicly available on The Cancer Imaging Archive. We analyzed theimage data using a Convolutional Neural Network (CNN), and we analyzed theclinical data using a fully connected Artificial Neural Network (ANN). We splitthis data into a training, validation, and testing partitions. We used the data totrain the ResNet101 CNN model which then classified whether a patient in theimage was likely to survive or not after 5 years. We trained the network for 100epochs on 90% of our dataset. Finally, 10% was used for cross-validation and10% was used for testing were used from the dataset.When patients are diagnosed with breast cancer, it is important for them tounderstand how serious the disease is, and they rely on experts like doctorsand radiologists to interpret a wide range of medical data in their record, whichincludes high quality images as well as clinical data and patient history. Byunderstanding the risk score of high and low risk, the patients are able to makethe right decisions about their future treatment plans. CNNs have the ability tomake use of medical images from a variety of sources to aid in the diagnosisand prognosis of diseases like cancer. Since breast cancer is so common inwomen, there are large publicly available datasets that contain a large numberof Magnetic Resonance Images (MRI). The I-SPY clinical trial was used to trainBreastNet, and it is a publicly available dataset which is available through TheCancer Imaging Archive.Fig.1-2 Two-dimensional Sagittal cross-sectionalimages of the MRI scans from the I-SPY1 ACRINClinical Trial, obtained from The Cancer ImagingArchiveMaterials and MethodsFig.3-6 Top left: Loss function over each trainingepoch. Bottom left: Accuracy function over eachtraining epoch. Top right: Confusion Matrix. BottomRight: Receiver Operating Curve, with AUC=0.9986Fig.7 BreastNet architecture that makes use of the fullimplementation of networks that accept clinical,image, and genetic datasets since each sub-model isspecialized for each data type. This framework couldbe used with ensemble learning techniques likeBoosting and Bagging[1] “How common is breast cancer?: Breast cancer statistics,”2021. [Online]. Available: https://www.cancer.org/cancer/breast-cancer/about/how-common-is-breast-cancer.html[2] David Newitt, Nola Hylton, on behalf of the I-SPY 1 Network and ACRIN 6657 Trial Team. (2016). Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.HdHpgJLK[3] P. Mukherjee, M. Zhou, E. Lee, A. Schicht, Y. Balagurunathan, S. Napel, R. Gillies, S. Wong, A. Thieme, A. Leung, and O. Gevaert, “A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomographyimage datasets,”Nature Machine Intelligence, vol. 2, no. 5, pp. 274–282, 2020.[4] “Biomarkers to guide treatment for metastatic breast cancer,” 2019, [Online]. Available: https://www.cancer.net/research-and-advocacy/asco-care-and-treatment-recommendations-patients/biomarkers-guide-treatment-metastatic-breast-cancer[5] Z. Wang, M. Li, H. Wang, H. Jiang, Y. Yao, H. Zhang, and J. Xin,“Breast cancer detection using extreme learning machine based on feature fusion with cnn deep features,”IEEE Access, vol. 7, pp.105 146–105 158, 2019.[6] S. U. C. A. Katzman, J.L., “Deepsurv: personalized treatment recommender system using a cox proportional hazards deep neural network, ”BMC Medical Research Methodology, vol. 18,no. 24, 2018. [Online]. Available: https://doi.org/10.1186/s12874-018-0482-1[7] L.-H. Cheng, T.-C. Hsu, and C. Lin, “Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction,”ScientificReports, 2021[8] D. R. Cox, “Regression models and life-tables,” Journal of the Royal Statistical Society. Series B (Methodological), vol.34, no.2, pp.187–220, 1972. [Online]. Available: http://www.jstor.org/stable/2985181[9] X. Zhou, Y. Li, and W. Liang, “Cnn-rnn based intelligent recommendation for online medical pre-diagnosis support,”IEEE/ACMTransactions on Computational Biology and Bioinformatics, vol. 18,no. 3, pp. 912–921, 2021.[10] G.R.XuY,“On splitting training and validation set: A comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalizationperformanceofsupervisedlearning.”JournalofAnalysisand Testing, vol. 2, p. 249–262, 2018. [Online]. Available:https://doi.org/10.1007/s41664-018-0068-2

UR-124 - BreastNet; Ensemble Deep Learning

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UR-124 - BreastNet; Ensemble Deep Learning

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