Biology subjects

Apte, A.

Publications and source records attributed to Apte, A..

8 recordsLinked to original sources

bioRxiv2025

Bidirectional Modulation of Somatostatin-expressing Interneurons in the Basolateral Amygdala Reduces Neuropathic Pain Perception in Mice

Neuropathic pain is characterized by mechanical allodynia and thermal (heat and cold) hypersensitivity, yet the underlying neural mechanisms remain poorly understood. This study examines the role of inhibitory interneurons in the basolateral amygdala (BLA) in modulating pain perception following nerve injury. Chemogenetic excitation of parvalbumin-positive (PV+) interneurons significantly alleviated mechanical allodynia but had minimal effects on thermal hypersensitivity. However, inhibition of PV+ interneurons did not produce significant changes in pain sensitivity, suggesting that reductions in perisomatic inhibition do not contribute to chronic pain states. In contrast, bidirectional modulation of somatostatin-positive (SST+) interneurons influenced pain perception in a modality-specific manner. Both excitation and inhibition of SST+ interneurons alleviated mechanical allodynia, indicating a potential compensatory role in nociceptive processing. Additionally, SST+ neuron excitation reduced cold hypersensitivity without affecting heat hypersensitivity, whereas inhibition improved heat hypersensitivity but not cold responses. These findings suggest that, in addition to PV+ neurons, SST+ interneurons in the BLA play a complex role in modulating neuropathic pain following nerve injury and may serve as a potential target for future neuromodulation interventions in chronic pain management.

neuroscience↗

bioRxiv2025

Artificial Intelligence Apps for Medical Image Analysis using pyCERR and Cancer Genomics Cloud

This work introduces a user-friendly, cloud-based software framework for conducting Artificial Intelligence (AI) analyses of medical images. The framework allows users to deploy AI-based workflows by customizing software and hardware dependencies. The components of our software framework include the Python-native Computational Environment for Radiological Research (pyCERR) platform for radiological image processing, Cancer Genomics Cloud (CGC) for accessing hardware resources and user management utilities for accessing images from data repositories and installing AI models and their dependencies. GNU-GPL copyright pyCERR was ported to Python from MATLAB-based CERR to enable researchers to organize, access, and transform metadata from high dimensional, multi-modal datasets to build cloud-compatible workflows for AI modeling in radiation therapy and medical image analysis. pyCERR provides an extensible data structure to accommodate metadata from commonly used medical imaging file formats and a viewer to allow for multi-modal visualization. Analysis modules are provided to facilitate cloud-compatible AI-based workflows for image segmentation, radiomics, DCE MRI analysis, radiotherapy dose-volume histogram-based features, and normal tissue complication and tumor control models for radiotherapy. Image processing utilities are provided to help train and infer convolutional neural network-based models for image segmentation, registration and transformation. The framework allows for round-trip analysis of imaging data, enabling users to apply AI models to their images on CGC and retrieve and review results on their local machine without requiring local installation of specialized software or GPU hardware. The deployed AI models can be accessed using APIs provided by CGC, enabling their use in a variety of programming languages. In summary, the presented framework facilitates end-to-end radiological image analysis and reproducible research, including pulling data from sources, training or inferring from an AI model, utilities for data management, visualization, and simplified access to image metadata.

bioinformatics↗

bioRxiv2022

Technical Note: STRATIS: A Cloud-enabled Software Toolbox for Radiotherapy and Imaging Analysis

PurposeRecent advances in computational resources, including software libraries and hardware, have enabled the use of high-dimensional, multi-modal datasets to build Artificial Intelligence (AI) models and workflows for radiation therapy and image analysis. The purpose of Software Toolbox for RAdioTherapy and Imaging analysiS (STRATIS) is to provide cloud-enabled, easy-to-share software workflows to train and deploy AI models for transparency and multi-institutional collaboration. MethodSTRATIS leverages open source medical image informatics software for application-specific analysis. Jupyter notebooks for AI modeling workflows are provided with Python language as the base kernel. In addition to Python, workflows use software written in other languages, such as MATLAB, GNU-Octave, R, and C++, with the help of bridge libraries. The workflows can be run on a cloud platform, local workstation, or an institutional HPC cluster. Computational environments are provided in the form of publicly available docker images -and build scripts for local Anaconda environments. Utilities provided with STRATIS simplify bookkeeping of associations between imaging objects and allow chaining data processing operations defined via a setting file for AI models. ResultsWorkflows available on STRATIS can be broadly categorized into image segmentation, deformable image registration, and outcomes modeling for radiotherapy toxicity and tumor control using radiomics and dosimetry features. The STRATIS-forge GitHub organization https://www.github.com/stratis-forge hosts build-scripts for Docker and Anaconda as well as Jupyter notebooks for analysis workflows. The software for building environments and workflow notebooks has open source-GNU-GPL copyright, and AI models retain the copyright chosen by their original developers. ConclusionSTRATIS enables researchers to deploy and share AI modeling workflows for radiotherapy and image analysis. STRATIS is publicly available on Terra.bios FireCloud platform with a pre-deployed computational environment and on GitHub organization for users pursuing local deployment.

bioinformatics↗

bioRxiv2021

Portable framework to deploy deep learning segmentation models for medical images

PurposeThis work presents a framework for deployment of deep learning image segmentation models for medical images across different operating systems and programming languages. MethodsComputational Environment for Radiological Research (CERR) platform was extended for deploying deep learning-based segmentation models to leverage CERRs existing functionality for radiological data import, transformation, management, and visualization. The framework is compatible with MATLAB as well as GNU Octave and Python for license-free use. Pre and post processing configurations including parameters for pre-processing images, population of channels, and post-processing segmentations was standardized using JSON format. CPU and GPU implementations of pre-trained deep learning segmentation models were packaged using Singularity containers for use in Linux and Conda environment archives for Windows, macOS and Linux operating systems. The framework accepts images in various formats including DICOM and CERRs planC and outputs segmentation in various formats including DICOM RTSTRUCT and planC objects. The ability to access the results readily in planC format enables visualization as well as radiomics and dosimetric analysis. The framework can be readily deployed in clinical software such as MIM via their extensions. ResultsThe open-source, GPL copyrighted framework developed in this work has been successfully used to deploy Deep Learning based segmentation models for five in-house developed and published models. These models span various treatment sites (H&N, Lung and Prostate) and modalities (CT, MR). Documentation for their usage and demo workflow is provided at https://github.com/cerr/CERR/wiki/Auto-Segmentation-models. The framework has also been used in clinical workflow for segmenting images for treatment planning and for segmenting publicly available large datasets for outcomes studies. ConclusionsThis work presented a comprehensive, open-source framework for deploying deep learning-based medical image segmentation models. The framework was used to translate the developed models to clinic as well as reproducible and consistent image segmentation across institutions, facilitating multi-institutional outcomes modeling studies.

bioinformatics↗

bioRxiv2019

Optimal Mass Transport for Robust Texture Analysis

The emerging field of radiomics, which consists of transforming standard-of-care images to quantifiable scalar statistics, endeavors to reveal the information hidden in these macroscopic images. This field of research has found different applications ranging from phenotyping and tumor classification to outcome prediction and treatment planning. Texture analysis, which often consists of reducing spatial texture matrices to summary scalar features, has been shown to be important in many of the latter applications. However, as pointed out in many studies, some of the derived texture statistics are strongly correlated and tend to contribute redundant information; and are also sensitive to the parameters used in their computation, e.g., the number of gray intensity levels. In the present study, we propose first to consider texture matrices, with an emphasis on gray-level co-occurrence matrix (GLCM), as a non-parametric multivariate objects. The proposed modeling approach avoids evaluating redundant and strongly correlated features and also prevents the feature processing steps. Then, via the Wasserstein distance from optimal mass transport theory, we propose to compare these spatial objects to identify computerized tomography slices with dental artifacts in head and neck cancer. We demonstrate the robustness of the proposed classification approach with respect to the GLCM extraction parameters and the size of the training set. Comparisons with the random forest classifier, which is constructed on scalar texture features, demonstrates the efficiency and robustness of the proposed algorithm.

bioengineering↗

bioRxiv2019

Reproducibility test of radiomics using network analysis and Wasserstein K-means algorithm

PurposeTo construct robust and validated radiomic predictive models, the development of a reliable method that can identify reproducible radiomic features robust to varying image acquisition methods and other scanner parameters should be preceded with rigorous validation. Due to the property of high correlation present between radiomic features, we hypothesize that reproducible radiomic features across different datasets that are obtained from different image acquisition settings preserve some level of connectivity between features in the form of a network.\n\nMethodsWe propose a regularized partial correlation network to identify robust and reproducible radiomic features. This approach was tested on two radiomic feature sets generated with two different reconstruction methods from a cohort of 47 lung cancer patients. The commonality of the resulting two networks was assessed. A largest common network component from the two networks was tested on phantom data consisting of 5 cancer samples. We further propose a novel K-means algorithm coupled with the optimal mass transport (OMT) theory to cluster samples. This approach following the regularized partial correlation analysis was tested on computed tomography (CT) scans from 77 head and neck cancer patients that were downloaded from The Cancer Imaging Archive (TCIA) and validated on CT scans from 83 head and neck cancer patients treated at our institution.\n\nResultsCommon radiomic features were found in relatively large network components between the resulting two partial correlation networks from a cohort of 47 lung cancer patients. The similarity of network components in terms of the common number of radiomic features was statistically significant. For phantom data, the Wasserstein distance on a largest common network component from the lung cancer data was much smaller than the Wasserstein distance on the same network using random radiomic features, implying the reliability of those radiomic features present in the network. Further analysis using the proposed Wasserstein K-means algorithm on TCIA head and neck cancer data showed that the resulting clusters separate tumor subsites and this was validated on our institution data.\n\nConclusionsWe showed that a network-based analysis enables identifying reproducible radiomic features. This was validated using phantom data and external data via the Wasserstein distance metric and the proposed Wasserstein K-means method.

biophysics↗

bioRxiv2019

Anatomically Informed 3D Printed CT phantoms: The First Step of a Pipeline To Identify Robust Quantitative Radiomic Features

PurposeThis study investigates the robustness of quantitative radiomic features derived from computed tomography (CT) images of a novel patient informed 3-D printed phantom, which captures the morphological heterogeneity of tumors and normal tissue observed on CT scans.\n\nMethodsUsing a novel voxel-based multi-material three-dimensional (3D) printer, an anthropomorphic phantom that was modeled after diseased tissue seen on 6 patient CT scans was manufactured. Four patients presented with pancreatic adenocarcinoma tumors (PDAC), 1 with non-small cell lung carcinoma (NSCLC) and 1 with advanced stage hepatic cirrhosis. The 5 tumors were segmented, extracted and then imbedded into CT images of the heterogenous portion of the cirrhotic liver. The composite scan of the implanted tumor within the background cirrhotic liver was then 3D printed. The resultant phantom was scanned sequentially, 30 times with a clinical CT scanner using a reference CT protocol. One hundred and four quantitative radiomic features were then extracted from images of each lesion to determine their repeatability. Repeatability of each radiomic feature was evaluated using the within subject coefficient of variation (wCV, %). A feature with a wCV (%) > 10% was considered as being unrepeatable. A subset of the repeatable features that were also found to be prognostic for lung and pancreatic cancers were then assessed for their percent deviation (pDV, %) from reference values. The reference values were those derived from the repeatability portion of this study. The assessment was conducted by re-scanning the phantom with 11 different clinically relevant sets of scanning parameters. Deviation of radiomic features derived from images of each tumor across all sets of scanning parameters was assessed using the percent deviation relative to the reference values.\n\nResultsTwenty nine of the 104 features presented with wCV (%) > 10%. The lack of repeatability was found to depend on tumor type. The only class of radiomic features with a wCV (%) < 10% were those calculated using the neighboring grey level dependence-based matrices (NGLDM). Notably, skewness, first information correlation, cluster shade, Haralick correlation, autocorrelation, busyness, complexity, high gray level zone emphasis, small area high gray level emphasis, large area low gray level emphasis, large area high gray level emphasis, short run high grey level emphasis, and valley radiomic features had wCV (%) values > 10% for select tumors within the phantom. Two radiomic features prognostic for NSCLC, energy and grey level non-uniformity, had pDVs (%) that exceeded 30% across all scanning techniques. The pDV (%) for the 4 radiomic features prognostic for PDAC tumors depended on tumor type and selected scanning parameter. Application of the lung kernel caused the largest pDVs (%). Scans acquired with the reduced tube current of 100 mA and reconstructed with the bone kernel yielded pDVs (%) within {+/-} 10%.\n\nConclusionWe demonstrated the feasibility with which patient informed 3D printed phantoms can be manufactured directly from lesions seen on CT scans, and demonstrate their potential use for the assessment of robust quantitative radiomic features.

biophysics↗

bioRxiv2019

Serum Alpha-2-Macroglobulin as an intrinsic radioprotective factor in patients undergoing thoracic radiation therapy

ObjectiveTo investigate the impact of alpha-2-macroglobulin (A2M), a suspected intrinsic radioprotectant, on radiation pneumonitis and esophagitis. Additionally, we establish multifactorial predictive models for pneumonitis and esophagitis.\n\nMaterials/MethodsBaseline A2M levels were obtained for 258 patients prior to thoracic radiotherapy (RT). Dose-volume characteristics were extracted from treatment plans. Spearmans correlation (Rs) test was used to correlate A2M levels, smoking status and dosimetric variables with toxicities. Esophagitis and pneumonitis prediction models were built using least absolute shrinkage and selection operator (LASSO) logistic regression on 1000 bootstrapped datasets. Models were built using 2/3 of the data for training and 1/3 for validation.\n\nResultsThere were 36 (14.0%) patients with grade [≥]2 pneumonitis and 61 (23.6%) with grade [≥]2 esophagitis. The median A2M level was 191 mg/dL (range: 94-511). Never/former/current smoker status was 47 (18.2%)/179 (69.4%)/32 (12.4%). We found a significant correlation between baseline A2M levels and esophagitis (Rs=-0.18/p=0.003) and between A2M and smoking status (former or current) (Rs=0.13/p=0.04) but not between A2M and pneumonitis. On univariate analysis, significant parameters for grade [≥]2 esophagitis included number of fractions (Rs=0.47/p<0.0001), treatment days (Rs=0.44/p<0.0001), chemotherapy use (Rs=0.40/p<0.0001), dose per fraction (Rs=-0.34/p<0.0001), total dose (Rs=0.29/p<0.0001), age (Rs=-0.22/p=0.0003), and several dosimetric variables in esophagus with Rs>0.5 (p<0.0001). For pneumonitis, significant clinical parameters were treatment days (Rs=0.24/p=0.0001), chemotherapy use (Rs=0.22/p=0.0004), number of fractions (Rs=0.21/p=0.0007), dose per fraction (Rs=-0.18/p=0.0035), and total dose (Rs=0.15/p=0.013). The most significant dosimetric variable in lung and heart was D70 (Rs=0.28/p<0.0001) and max dose (Rs=0.27/p<0.0001), respectively. LASSO bootstrap logistic regression models on the validation data resulted in the area under the receiver operating characteristic curve of 0.84 and 0.75 for esophagitis and pneumonitis, respectively.\n\nConclusionOur findings show an association of higher A2M values with lower risk of radiation esophagitis and smoking status. Multivariate predictive models also confirmed a role of heart dose in the risk of pneumonitis.

cancer biology↗