Amogh Hiremath - Academia.edu (original) (raw)

Papers by Amogh Hiremath

Journal of clinical oncology, Jun 1, 2024

Computers in biology and medicine, Jul 1, 2024

The Journal of urology/The journal of urology, May 1, 2024

Cancer research, Mar 22, 2024

Frontiers in Oncology, Sep 4, 2023

The aim of this study was to quantify radiomic changes in prostate cancer (PCa) progression on se... more The aim of this study was to quantify radiomic changes in prostate cancer (PCa) progression on serial MRI among patients on active surveillance (AS) and evaluate their association with pathologic progression on biopsy. Methods: This retrospective study comprised N = 121 biopsy-proven PCa patients on AS at a single institution, of whom N = 50 at baseline conformed to the inclusion criteria. ISUP Gleason Grade Groups (GGG) were obtained from 12-core TRUS-guided systematic biopsies at baseline and follow-up. A biopsy upgrade (AS+) was defined as an increase in GGG (or in number of positive cores) and no upgrade (AS−) was defined when GGG remained the same during a median period of 18 months. Of N = 50 patients at baseline, N = 30 had MRI scans available at follow-up (median interval = 18 months) and were included for delta radiomic analysis. A total of 252 radiomic features were extracted from the PCa region of interest identified by board-certified radiologists on 3T biparametric MRI [T2-weighted (T2W) and apparent diffusion coefficient (ADC)]. Delta radiomic features were computed as the difference of radiomic feature between baseline and follow-up scans. The association of AS+ with age, prostate-specific antigen (PSA), Prostate Imaging Reporting and Data System (PIRADS v2.1) score, and tumor size was evaluated at baseline and follow-up. Various prediction models were built using random forest (RF) classifier within a threefold cross-validation framework leveraging baseline radiomics (C br), baseline radiomics + baseline clinical (C brbcl), delta radiomics (C Dr), delta radiomics + baseline clinical (C Drbcl), and delta radiomics + delta clinical (C DrDcl).

Journal of Clinical Oncology, Jun 1, 2023

European Radiology, Jul 23, 2020

Objectives-To evaluate short-term test-retest repeatability of a deep learning architecture (U-Ne... more Objectives-To evaluate short-term test-retest repeatability of a deep learning architecture (U-Net) in slice-and lesion-level detection and segmentation of clinically significant prostate cancer (csPCa: Gleason grade group > 1) using diffusion-weighted imaging fitted with monoexponential function, ADC m .

Social Science Research Network, 2021

Objective: The disease COVID-19 has caused a widespread global pandemic with ~3.93 million deaths... more Objective: The disease COVID-19 has caused a widespread global pandemic with ~3.93 million deaths worldwide. In this work, we present three models- Radiomics (MRM), Clinical (MCM), and combined Clinical-Radiomics (MRCM) nomogram to predict COVID-19 positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. Method: We performed a retrospective multicohort study of individuals with COVID-19 positive findings for a total of 980 patients from 2 different institutions (Renmin hospital of Wuhan University, D1 =787 and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D1 T (N=473), and 40% test set D1 V (N=314). The patients from institution-2 were used for an independent validation test set D2 V(N=110). A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first-order and higher-order Radiomic textural features. The top Radiomic and clinical features were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) with an optimal binomial regression model within D1 T. Results: The 3 out of the top 5 features identified using D1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total infection size on the CT scan and the total intensity of the COVID consolidations. The Radiomics Model (MRM ) was constructed using the Radiomic Score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 [0.709-0.799] on D1 T, 0.836 on D1 V, and 0.748 D2 V. The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 [0.743-0.825] on D1 T, 0.813 on D1 V, and 0.723 on D2 V. Finally, the combined model, MRCM integrating Radiomic Score, age, LDH and ALB, yielded an AUC of 0.814 [0.774-0.853] on D1 T, 0.847 on D1 V, and 0.772 on D2 V. The MRCM had an overall improvement in the performance of ~3.77% (D1 T: p = 0.0003; D1 V p= 0.0165; D2 V : p = 0.024) over MCM Conclusion: The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially requiring mechanical ventilation. Funding: National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defense, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation. Declaration of Interest: AM reports grants from National Cancer Institute of the National Institutes of Health, grants from National Center for Research Resources, grants from VA Merit Review Award, grants from DOD Cancer Investigator-Initiated Translational Research Award, during the conduct of the study; grants from DOD Prostate Cancer Idea Development Award, grants from DOD Peer Reviewed Cancer Research Program, grants from National Institute of Diabetes and Digestive and Kidney Diseases , grants from the Ohio Third Frontier Technology Validation Fund, grants from the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering and the Clinical and Translational Science Award Program (CTSA) at Case Western Reserve University, grants from Department of Defense Peer Reviewed Cancer Research Program (PRCRP) Career Development Award, grants from Dana Foundation David Mahoney Neuroimaging Program. Ethical Approval: The study conformed to HIPAA guidelines was approved by University Hospitals, Cleveland (STUDY20200463), and the Ethics committee of the Renmin Hospital of Wuhan University (ethics number: V1.0).

Medical Physics

BackgroundAccurate delineations of regions of interest (ROIs) on multi‐parametric magnetic resona... more BackgroundAccurate delineations of regions of interest (ROIs) on multi‐parametric magnetic resonance imaging (mpMRI) are crucial for development of automated, machine learning‐based prostate cancer (PCa) detection and segmentation models. However, manual ROI delineations are labor‐intensive and susceptible to inter‐reader variability. Histopathology images from radical prostatectomy (RP) represent the “gold standard” in terms of the delineation of disease extents, for example, PCa, prostatitis, and benign prostatic hyperplasia (BPH). Co‐registering digitized histopathology images onto pre‐operative mpMRI enables automated mapping of the ground truth disease extents onto mpMRI, thus enabling the development of machine learning tools for PCa detection and risk stratification. Still, MRI‐histopathology co‐registration is challenging due to various artifacts and large deformation between in vivo MRI and ex vivo whole‐mount histopathology images (WMHs). Furthermore, the artifacts on WMHs...

Medical Imaging 2022: Computer-Aided Diagnosis, Apr 4, 2022

Circulation

Introduction: Catheter-based ablation is an increasingly utilized management strategy for atrial ... more Introduction: Catheter-based ablation is an increasingly utilized management strategy for atrial fibrillation (AF); however, a major problem is AF recurrence post-ablation. There is increasing interest in the use of advanced image analysis to better understand differences in left atrial (LA) shape that may be associated with a higher recurrence risk and potential sites of recurrence. Hypothesis: Factors which lead to the post-ablation recurrence prompt LA differential remodeling. The significant shape difference regions among AF recurrence (AF+) and AF non-recurrence (AF-) patients can be identified from pre-ablation CT scans. Those regions could potentially represent future sites of recurrence and hence could be targets for ablation. Methods: This study included pre-ablation CT-scans of 51 AF+ and 51 AF- patients. Two separate atlases were created by registering Left atrial CT volumes of AF+ and AF- patients to their representative templates (T+ and T- respectively). The atlases we...

Medical Imaging 2022: Computer-Aided Diagnosis, 2022

SSRN Electronic Journal, 2021

Sound waves pervade the entire universe and as humans our hearing capacity permits us to detect o... more Sound waves pervade the entire universe and as humans our hearing capacity permits us to detect only a limited set of waves. Machines, however don't have this limitation. Imagine a machine that can sense a knock on the door, someone breaking into your home, detect accidents by itself and take decisions by sensing different sounds in the environ- ment! Our project, Never Ending Learning of Sound aims to make the machine continuously learn sounds that exist, by crawling the entire web. This will help the machine to under- stand, sense, categorize and model the relationships between different sounds. It is an effort to build one of the largest struc- tured sound database. This is a difficult problem due to three aspects. 1. Amount of Data needed to be processed for sound 2. Interference of Sounds 3. Validation of the results obtained with minimum human intervention