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Building Classification Models with Combined Biomarker Tests： Application to Early Detection of Liver Cancer

Journal of Statistical Science and Application 2017年第3期5卷 91-103页

作者： Dion Chen surbhi jain Ying-Hsu Su Wei Song Biostatistics Janssen R&D LLC Spring House PA 19477 USA Biomarkers JBS Science Inc.Doylestown PA 18902 USA Biomarkers The Baruch S. Blumberg Institute Doylestown PA 18902 USA

Early detection of hepatocellular carcinoma （HCC） is critical for the effective treatment. Alpha fetoprotein （AFP） serum level is currently used for HCC screening, but the cutoff of the AFP test has limited sensitivity （-50%）, indicating a high false negative rate. We have successfully demonstrated that cancer derived DNA biomarkers can be detected in urine of patients with cancer and can be used for the early detection of cancer （jain et al., 2015; Lin et al., 2011; Song et al., 2012; Su, Lin, Song, ＆ jain, 2014; Su, Wang, Norton, Brenner, ＆ Block, 2008）. By combining urine biomarkers （uBMK） values and serum AFP （sAFP） level, a new classification model has been proposed for more efficient HCC screening. Several criterions have been discussed to optimal the cutoff for uBMK score and sAFP score. A joint distribution of sAFP and uBMK with point mass has been fitted using maximum likelihood method. Numerical results show that the sAFP data and uBMK data are very well described by proposed model. A tree-structured sequential test can be optimized by selecting the cutoffs. Bootstrap simulations also show the robust classification results with the optimal cuto~..

关键词： Classification Model Biomarker Data Analysis Joint Distribution Sensitivity Specificity LiverCancer.

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Detection of urine DNA markers for monitoring recurrent hepatocellular carcinoma

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Hepatoma Research 2017年第6期3卷 105-111页

作者： Hie-Won Hann surbhi jain Grace Park Jamin DSteffen Wei Song Ying-Hsiu Su Liver Disease Prevention Center Division of Gastroenterology and HepatologyThomas Jefferson University HospitalPhiladelphiaPA19107USA JBS Science Inc.DoylestownPA18902USA The Baruch S.Blumberg Institute DoylestownPA18902USA.

Aim: This study aimed to explore the potential of detecting hepatocellular carcinoma (HCC)-associated DNA markers, TP53249T mutations and aberrant methylation of RASSF1A and GSTP1 genes, for monitoring HCC recurrence. HCC remains a leading cause of death worldwide, with one of the fastest growing incidence rates in the US. While treatment options are available and new ones emerging, there remains a poor prognosis of this disease mostly due to its late diagnosis and high recurrence rate. Although there are no specific guidelines addressing how HCC recurrence should be monitored, recurrence is usually monitored by serum-alpha fetal protein and imaging methods such as magnetic resonance imaging (MRI). However, early detection of recurrent HCC remains limited, particularly at the site of treated lesion. Methods: Here, the authors followed 10 patients that were treated for a primary HCC, and monitored for months or years later. At these follow-up visits, urine was collected and tested retrospectively for 3 DNA biomarkers that associate with HCC development. Results:This 10-patient study compared detection of urine DNA markers with MRI for monitoring HCC recurrence. Five patients were confirmed by MRI for recurrence, and all 5 had detectable DNA biomarkers up to 9 months before recurrence confirmation by MRI. Conclusion:Overall, this suggests that detection of HCC-associated DNA markers in urine could provide a promising tool to complement detection of recurrent HCC by imaging.

关键词： Hepatocellular carcinoma recurrence circulating-tumor DNA liver cancer biomarker urine tumor marker hepatitis B virus

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Spatial Attention Integrated EfficientNet Architecture for Breast Cancer Classification with Explainable AI

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Computers, Materials & Continua 2024年第9期80卷 5029-5045页

作者： Sannasi Chakravarthy Bharanidharan Nagarajan surbhi Bhatia Khan Vinoth Kumar Venkatesan Mahesh Thyluru Ramakrishna Ahlam AlMusharraf Khursheed Aurungzeb Department of Electronics and Communication Engineering Bannari Amman Institute of TechnologySathyamangalam638402India School of Computer Science Engineering and Information Systems(SCORE) Vellore Institute of TechnologyVellore632014India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India Department of Management College of Business AdministrationPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpura140401India

Breast cancer is a type of cancer responsible for higher mortality rates among *** cruelty of breast cancer always requires a promising approach for its earlier *** light of this,the proposed research leverages the representation ability of pretrained EfficientNet-B0 model and the classification ability of the XGBoost model for the binary classification of breast *** addition,the above transfer learning model is modified in such a way that it will focus more on tumor cells in the input ***,the work proposed an EfficientNet-B0 having a Spatial Attention Layer with XGBoost(ESA-XGBNet)for binary classification of *** this,the work is trained,tested,and validated using original and augmented mammogram images of three public datasets namely CBIS-DDSM,INbreast,and MIAS *** accuracy of 97.585%(CBISDDSM),98.255%(INbreast),and 98.91%(MIAS)is obtained using the proposed ESA-XGBNet architecture as compared with the existing ***,the decision-making of the proposed ESA-XGBNet architecture is visualized and validated using the Attention Guided GradCAM-based Explainable AI technique.

关键词： EfficientNet mammograms breast cancer Explainable AI deep-learning transfer learning

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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Computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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