P. B, Priscilla Dinkar Moyya, Mythili Asaithambi, Anadh Kilpattu
Breast cancer is the most predominant disease and foremost cause of cancer deaths in women worldwide, with treatment plans varying regardless of the grade and biology of the tumor. Neoadjuvant chemotherapy (NAC) is the standard clinical implementation to retrench the tumor size and escalate the breast-conserving rate. Dynamic contrast enhanced MR imaging (DCE-MRI) is an effective modality in analyzing the response during NAC treatment. However lower-grade cancer patients are slow growing tumors with a better prognosis, but higher-grade cancer patients aggressively grow and require effective treatment. So, it is necessary to investigate the grade specific response information during the NAC treatment. In this work, analysis of NAC treatment response on breast cancer patients is performed by investigating the low- and high-grade cancer patients separately using DCE MR images. Twenty-six patient data with three visits of NAC treatment is obtained from QIN BREAST and QIN BREAST-02 datasets from the openly available TCIA database. The mean intensity (MI) value is calculated from manually segmented tumor volumes at different visits for both low- and high-grade cancer patients. The results demonstrate that mean intensity values showed a statistical difference between Visit 1 & 3 in both low- and high-grade patients during NAC with p ≤ 0.05. The percentage difference in mean intensity value between Visit 1 & 3 of high-grade subjects is observed to be high compared to low-grade subjects. Hence it appears that the high-grade breast cancer patients respond well to NAC treatment response compared to low-grade breast cancer patients.
{"title":"INVESTIGATION OF LOW- AND HIGH-GRADE TUMORS IN EVALUATING THE NEOADJUVANT CHEMOTHERAPY TREATMENT RESPONSE USING BREAST DCE MR IMAGES","authors":"P. B, Priscilla Dinkar Moyya, Mythili Asaithambi, Anadh Kilpattu","doi":"10.34107/nsjx7335104","DOIUrl":"https://doi.org/10.34107/nsjx7335104","url":null,"abstract":"Breast cancer is the most predominant disease and foremost cause of cancer deaths in women worldwide, with treatment plans varying regardless of the grade and biology of the tumor. Neoadjuvant chemotherapy (NAC) is the standard clinical implementation to retrench the tumor size and escalate the breast-conserving rate. Dynamic contrast enhanced MR imaging (DCE-MRI) is an effective modality in analyzing the response during NAC treatment. However lower-grade cancer patients are slow growing tumors with a better prognosis, but higher-grade cancer patients aggressively grow and require effective treatment. So, it is necessary to investigate the grade specific response information during the NAC treatment. In this work, analysis of NAC treatment response on breast cancer patients is performed by investigating the low- and high-grade cancer patients separately using DCE MR images. Twenty-six patient data with three visits of NAC treatment is obtained from QIN BREAST and QIN BREAST-02 datasets from the openly available TCIA database. The mean intensity (MI) value is calculated from manually segmented tumor volumes at different visits for both low- and high-grade cancer patients. The results demonstrate that mean intensity values showed a statistical difference between Visit 1 & 3 in both low- and high-grade patients during NAC with p ≤ 0.05. The percentage difference in mean intensity value between Visit 1 & 3 of high-grade subjects is observed to be high compared to low-grade subjects. Hence it appears that the high-grade breast cancer patients respond well to NAC treatment response compared to low-grade breast cancer patients.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46090216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Estrogen Receptor (ER) is a molecular biomarker that plays an important role in evaluating the Neoadjuvant Chemotherapy (NAC) treatment response of breast cancer patients. ER (-) breast cancer patients have better tumor response rates than ER (+) patients due to NAC and the result of ER status could change after NAC. However, there are limited studies on the analysis of NAC treatment response using ER status. Further, manual quantification of treatment response is challenging and inconsistent across raters. In this work, an attempt has been made to objectively quantify the radiological differences of Dynamic Contrast Enhanced (DCE) MR images in ER (-) and ER (+) patients due to NAC using Gabor filter derived Anisotropy Index (AI). The images (113 subjects at 4 visits of NAC treatment) used in this study are obtained from the publicly available I-SPY1 dataset. Gabor filter bank is designed with 5 scales and 7 orientations, and AI is calculated from each Gabor energy within the patient group. Results show that AI values can statistically (p < 0.05) differentiate the radiological differences in ER (-) and ER (+) patients due to NAC. The percentage difference in the mean AI values of Visit 1 Vs Visit 4, Visit 1 Vs Visit 3, and Visit 2 Vs Visit 4 is high in ER (-) compared to ER (+) patients. Thus, Gabor filter derived AI could be used as an objective measure in evaluating NAC treatment response in ER (-) and ER (+) patients.
{"title":"ANALYSIS OF NEOADJUVANT CHEMOTHERAPY TREATMENT RESPONSE IN BREAST DCE MRI PATIENTS BASED ON ESTROGEN RECEPTOR STATUS AND GABOR FILTER DERIVED ANISOTROPY INDEX","authors":"Priscilla Dinkar Moyya, Mythili Asaithambi, Anandh Kilpattu Ramaniharan","doi":"10.34107/nsjx733596","DOIUrl":"https://doi.org/10.34107/nsjx733596","url":null,"abstract":"Estrogen Receptor (ER) is a molecular biomarker that plays an important role in evaluating the Neoadjuvant Chemotherapy (NAC) treatment response of breast cancer patients. ER (-) breast cancer patients have better tumor response rates than ER (+) patients due to NAC and the result of ER status could change after NAC. However, there are limited studies on the analysis of NAC treatment response using ER status. Further, manual quantification of treatment response is challenging and inconsistent across raters. In this work, an attempt has been made to objectively quantify the radiological differences of Dynamic Contrast Enhanced (DCE) MR images in ER (-) and ER (+) patients due to NAC using Gabor filter derived Anisotropy Index (AI). The images (113 subjects at 4 visits of NAC treatment) used in this study are obtained from the publicly available I-SPY1 dataset. Gabor filter bank is designed with 5 scales and 7 orientations, and AI is calculated from each Gabor energy within the patient group. Results show that AI values can statistically (p < 0.05) differentiate the radiological differences in ER (-) and ER (+) patients due to NAC. The percentage difference in the mean AI values of Visit 1 Vs Visit 4, Visit 1 Vs Visit 3, and Visit 2 Vs Visit 4 is high in ER (-) compared to ER (+) patients. Thus, Gabor filter derived AI could be used as an objective measure in evaluating NAC treatment response in ER (-) and ER (+) patients.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46621749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shabina Shaikh, Nagarajan Ganapathy, R. Swaminathan
The Corpus Callosum (CC) is a large white matter bundle that connects the left and right cerebral hemispheres of the human brain. It is susceptible to atrophy as Alzheimer’s disease progresses. The robust segmentation of CC allows quantitative investigation of its structural changes. However, deep learning-based CC segmentation is less explored. In this work, an improved UNet model is proposed for CC segmentation from two-dimensional T1-weighted mid-sagittal brain MRI. For this, mid-sagittal scans (n = 184) from the publicly available Open Access Series of Imaging Studies (OASIS) brain MRI database are used. The images are fed to an improved UNet++ network. The architecture contains a fully convolutional network with two paths, contracting and extracting, that are connected in a U-shape to automatically extract spatial information. Leave one out Cross-Validation (LooCV) method is used to evaluate the robustness of the proposed method. Results show that the proposed approach is able to segment CC from MR images. The proposed method yields the Dice score of 98.43%, and Jaccard index of 98.53%. The improved UNet++ model obtained the highest sensitivity of 99.21% for AD conditions. Further, the performance of the proposed model has been validated against the state-of-the-art methods. Thus, the proposed approach could be useful for the segmentation of MR images in clinical condition.
{"title":"AUTOMATED SEGMENTATION OF CORPUS CALLOSUM IN BRAIN MR IMAGES IN ALZHEIMER’S CONDITIONS USING IMPROVED UNET++ MODEL","authors":"Shabina Shaikh, Nagarajan Ganapathy, R. Swaminathan","doi":"10.34107/nsjx733589","DOIUrl":"https://doi.org/10.34107/nsjx733589","url":null,"abstract":"The Corpus Callosum (CC) is a large white matter bundle that connects the left and right cerebral hemispheres of the human brain. It is susceptible to atrophy as Alzheimer’s disease progresses. The robust segmentation of CC allows quantitative investigation of its structural changes. However, deep learning-based CC segmentation is less explored. In this work, an improved UNet model is proposed for CC segmentation from two-dimensional T1-weighted mid-sagittal brain MRI. For this, mid-sagittal scans (n = 184) from the publicly available Open Access Series of Imaging Studies (OASIS) brain MRI database are used. The images are fed to an improved UNet++ network. The architecture contains a fully convolutional network with two paths, contracting and extracting, that are connected in a U-shape to automatically extract spatial information. Leave one out Cross-Validation (LooCV) method is used to evaluate the robustness of the proposed method. Results show that the proposed approach is able to segment CC from MR images. The proposed method yields the Dice score of 98.43%, and Jaccard index of 98.53%. The improved UNet++ model obtained the highest sensitivity of 99.21% for AD conditions. Further, the performance of the proposed model has been validated against the state-of-the-art methods. Thus, the proposed approach could be useful for the segmentation of MR images in clinical condition.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49414793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alzheimer’s Disease (AD) is a progressive irreversible neurodegenerative disorder which involves the deformations in brain sub-anatomic regions. Recent studies suggest that these deformations could be characterized using bi-planar information extracted from structural Magnetic Resonance (MR) image features. However, analysis and fusion of these bi-planar features have been a challenging task in AD differentiation. In this study, an attempt has been made to fuse the characteristics of axial and sagittal view MR images using Canonical Correlation Analysis (CCA) for the differentiation of Healthy Controls (HC) and AD. For this, MR brain images obtained from a public database are skull stripped and spatially registered. Morphometric features are extracted from the pre-processed mid-sagittal and mid-axial images using histogram of oriented gradients. Further, these extracted features are fused using CCA. The performance of classifier is analyzed for the variations in canonical component dimensions. Results indicate that the morphometric feature spaces extracted from sagittal and axial planes individually overlap for HC and AD. The proposed CCA based fusion of sagittal and axial features exhibit variations between HC and AD images for a canonical feature dimension of 30. Performance of the adopted approach confirms that the bi-planar feature fusion is essential for the differentiation of AD.
{"title":"DIFFERENTIATION OF MR BRAIN ALZHEIMER IMAGES USING BI-PLANAR CANONICAL CORRELATION BASED FEATURE FUSION","authors":"Sreelakshmi Shaji, R. Swaminathan, R. Palanisamy","doi":"10.34107/nsjx733582","DOIUrl":"https://doi.org/10.34107/nsjx733582","url":null,"abstract":"Alzheimer’s Disease (AD) is a progressive irreversible neurodegenerative disorder which involves the deformations in brain sub-anatomic regions. Recent studies suggest that these deformations could be characterized using bi-planar information extracted from structural Magnetic Resonance (MR) image features. However, analysis and fusion of these bi-planar features have been a challenging task in AD differentiation. In this study, an attempt has been made to fuse the characteristics of axial and sagittal view MR images using Canonical Correlation Analysis (CCA) for the differentiation of Healthy Controls (HC) and AD. For this, MR brain images obtained from a public database are skull stripped and spatially registered. Morphometric features are extracted from the pre-processed mid-sagittal and mid-axial images using histogram of oriented gradients. Further, these extracted features are fused using CCA. The performance of classifier is analyzed for the variations in canonical component dimensions. Results indicate that the morphometric feature spaces extracted from sagittal and axial planes individually overlap for HC and AD. The proposed CCA based fusion of sagittal and axial features exhibit variations between HC and AD images for a canonical feature dimension of 30. Performance of the adopted approach confirms that the bi-planar feature fusion is essential for the differentiation of AD.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44982406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yedukomdala Rao Veeranki, Nagarajan Ganapathy, R. Swaminathan
In this work, an attempt has been made to classify dichotomous emotional states using Electrodermal activity (EDA) and geometric features. For this, the annotated happy and sad EDA is obtained from the online public database. The EDA is subjected to discrete Fourier transform, and Fourier coefficients in the complex plane are obtained. The envelope of the complex plane is identified using the α-shape method. Five geometric features, namely center of gravity, eccentricity, convexity, rectangularity, and convex hull area are computed from the envelope and statistical analysis is performed. Two machine-learning algorithms, namely random forest (RF) and support vector machine, are considered for the classification. The results show that the proposed approach is able to classify the dichotomous emotional states. The rectangularity feature is found to be distinct and shows a statistically significant difference between the happy and sad emotional states (p<0.05). The RF classifier yields the highest F-m and AUC of 87.8% and 93.8%, respectively in differentiating emotional states. Thus, it appears that the proposed method could be used to understand the neurological, psychiatric, and biobehavioral mechanisms associated with happy and sad emotional states.
{"title":"CHARACTERIZATION OF DICHOTOMOUS EMOTIONAL STATES USING ELECTRODERMAL ACTIVITY BASED GEOMETRIC FEATURES","authors":"Yedukomdala Rao Veeranki, Nagarajan Ganapathy, R. Swaminathan","doi":"10.34107/nsjx733575","DOIUrl":"https://doi.org/10.34107/nsjx733575","url":null,"abstract":"In this work, an attempt has been made to classify dichotomous emotional states using Electrodermal activity (EDA) and geometric features. For this, the annotated happy and sad EDA is obtained from the online public database. The EDA is subjected to discrete Fourier transform, and Fourier coefficients in the complex plane are obtained. The envelope of the complex plane is identified using the α-shape method. Five geometric features, namely center of gravity, eccentricity, convexity, rectangularity, and convex hull area are computed from the envelope and statistical analysis is performed. Two machine-learning algorithms, namely random forest (RF) and support vector machine, are considered for the classification. The results show that the proposed approach is able to classify the dichotomous emotional states. The rectangularity feature is found to be distinct and shows a statistically significant difference between the happy and sad emotional states (p<0.05). The RF classifier yields the highest F-m and AUC of 87.8% and 93.8%, respectively in differentiating emotional states. Thus, it appears that the proposed method could be used to understand the neurological, psychiatric, and biobehavioral mechanisms associated with happy and sad emotional states.","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43117508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.11648/j.bs.20220801.16
Abdul Rehman Khokhar, Qurrat-ul Ain Rehman, M. Hussain
{"title":"Test Item Analysis of MCQS of Medical Physiology: Summative Assessment","authors":"Abdul Rehman Khokhar, Qurrat-ul Ain Rehman, M. Hussain","doi":"10.11648/j.bs.20220801.16","DOIUrl":"https://doi.org/10.11648/j.bs.20220801.16","url":null,"abstract":"","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77652554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.11648/j.bs.20220801.17
C. Amadi, S. Lawson, Bright Amadi, E. Agbo
{"title":"Correlation of Plasma Albumin Status with Markers of Hepato-biliary Dysfunction and Systemic Inflammation Among COVID-19 Patients","authors":"C. Amadi, S. Lawson, Bright Amadi, E. Agbo","doi":"10.11648/j.bs.20220801.17","DOIUrl":"https://doi.org/10.11648/j.bs.20220801.17","url":null,"abstract":"","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73580225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.11648/j.bs.20220801.18
Mohammad Abdullah Al Muti, Naresh Kumar Roy, Syed Shamsul Arefin, A. Al Mamun, Mohi Uddin Aslam
{"title":"Outcomes of Total Knee Replacement: A Prospective Observational Study in Bangladesh","authors":"Mohammad Abdullah Al Muti, Naresh Kumar Roy, Syed Shamsul Arefin, A. Al Mamun, Mohi Uddin Aslam","doi":"10.11648/j.bs.20220801.18","DOIUrl":"https://doi.org/10.11648/j.bs.20220801.18","url":null,"abstract":"","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73223592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.11648/j.bs.20220801.15
Charisse Begonia Ferrer, Marie Ellaine Nicer Velasquez
{"title":"Association of Gastrointestinal Manifestations and Laboratory Abnormalities on Clinical Outcomes of COVID-19 Patients in a Tertiary Hospital","authors":"Charisse Begonia Ferrer, Marie Ellaine Nicer Velasquez","doi":"10.11648/j.bs.20220801.15","DOIUrl":"https://doi.org/10.11648/j.bs.20220801.15","url":null,"abstract":"","PeriodicalId":75599,"journal":{"name":"Biomedical sciences instrumentation","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85322465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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