About

Sandip Biswal, MD, is a Professor (Tenure) in the Department of Radiology at the University of Wisconsin School of Medicine and Public Health. His academic background includes education at Harvard Medical School and the Massachusetts Institute of Technology, completed in 1996. He has undergone internship at Columbia University Medical Center in 2001, residency at Stanford University in 2001, and fellowships at the University of California, Los Angeles in 2003 and at the University of California, San Diego in 2002. Dr. Biswal specializes in Musculoskeletal Imaging and Intervention, contributing to the department's focus on advanced imaging techniques and interventional radiology. His work is recognized within the university's radiology community, and he has been involved in faculty activities such as participating in the inaugural joint symposium with the Netherlands. His professional profile indicates a strong background in medical imaging, with a focus on musculoskeletal applications, and he is actively engaged in the academic and clinical missions of the Department of Radiology at UW–Madison.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Medicine
• Radiology
• Surgery
• Pathology
• Nuclear medicine

Selected publications

• Identification of peripheral pain generators with sigma-1 receptor Positron Emission Tomography/Magnetic Resonance Imaging in complex regional pain syndrome: initial study in a prospective trial

  Pain · 2026-01-15 · 1 citations

  articleOpen access

  ABSTRACT: Complex regional pain syndrome (CRPS) is a poorly understood chronic pain condition of the extremities presenting severe pain disproportionate to the causative injury. Owing to its heterogeneous clinical presentation and the lack of specific diagnostic tests, CRPS is often a diagnostic and therapeutic challenge. Early diagnosis and treatment of CRPS improve quality of life and delay disease progression. Identification of focal peripheral pain generators would provide an opportunity for targeted treatments with more limited side effects. A highly selective sigma-1 receptor positron emission tomography (PET) radioligand, [18F]FTC-146, has been developed and has shown promise in identifying inflammatory or nociceptive processes. Our aim was to investigate the utility of [18F]FTC-146 PET/MRI for identifying peripheral pain generators and assessing its impact on subsequent clinical management of patients with CRPS. This single-center study enrolled 15 subjects with a clinical diagnosis of CRPS to undergo [18F]FTC-146 PET/MRI. PET/MRI findings were reviewed and discussed with referring pain specialists. Pain scores and subsequent changes in pain management for each patient were prospectively noted. Potential pain generators were observed in 9 of 15 subjects. Subsequent pain treatments guided by abnormally increased foci of uptake on [18F]FTC-146 PET/MRI resulted in an average 5-point improvement in pain score in 80% (7/9) of subjects. Overall, [18F]FTC-146 PET/MRI was able to identify potential peripheral pain generators in the affected limbs of subjects with CRPS and subsequently guided targeted treatments that resulted in varying degrees of improvement in subjective pain scores.

  PublisherOA PDFDOI

• Localizing Nerve Injury, Defining Injury Severity, and Estimating Prognosis (Nerve SPACE 2025)

  Journal of Hand Surgery Global Online · 2026-01-28

  articleOpen access

  Background: Localizing nerve injury, defining injury severity, and estimating prognosis are critical factors in surgical decision-making when indicating patients for operative intervention following traumatic nerve injury. Where are we now?: Current methods for localizing nerve injury and determining severity of injury include physical examination, electrodiagnostic studies, imaging including ultrasound and magnetic resonance imaging, and surgical exploration. However, these methods remain suboptimal, especially in cases of segmental or multilevel injury as is often seen in blunt force trauma as well as in cases of partial (axonotmetic) injury. A period of observation is often required to determine if spontaneous recovery will occur. In neurotmetic injuries, it is challenging to accurately determine the zone of injury intraoperatively to ensure that reconstruction is performed using healthy, viable nerve. As a result of these shortcomings, it has been difficult to accurately and consistently classify nerve injury according to location and severity which has resulted in difficulty estimating the prognosis for many injuries. Where do we need to go?: Better diagnostic methods are needed to be able to accurately determine the location of nerve injury to direct surgical intervention and determine prognosis, especially in blunt, ballistic, multilevel, or segmental injuries. Additionally, improved methods are needed to evaluate partial axonotmetic injuries in which the epineurium remains grossly intact on inspection intraoperatively, but with varying degrees of axonotmetic injury within the nerve. This includes a need for both noninvasive preoperative imaging and biomarkers as well as intraoperative modalities to more accurately determine the degree of intraneural damage and assist in preoperative indications and intraoperative decision-making. Improving these diagnostic modalities will allow classification of injuries by location and severity on a more consistent and accurate basis, leading to improved ability to estimate prognosis, surgical indications, and intraoperative decision-making. How do we get there?: Emerging diagnostic modalities, including simultaneous positron emission tomography and magnetic resonance imaging, nerve-specific fluorescence imaging, quantitative ultrasound and magnetic resonance imaging, peripheral nerve diffusion tensor imaging, magnetic resonance neurography, polarization-sensitive optical coherence tomography, and serum biomarkers for peripheral nerve injury, offer promising advances that may help better localize and define injury severity in peripheral nerve injury. More research and funding are needed to better understand how best to apply each of these modalities for traumatic nerve injury, leading to broader adoption, more accurate classification and consistent reporting of data that can be linked to patient outcomes and ultimately help improve our ability to estimate prognosis after nerve injury.

  PublisherDOI

• [18F]fluorodeoxyglucose PET/MRI for nononcological musculoskeletal indications: reference uptake values in the asymptomatic lumbar spine and example comparison to a low back pain patient

  Nuclear Medicine Communications · 2026-04-27

  articleOpen access

  OBJECTIVE: To assess normal [18F]fluorodeoxyglucose ([18F]FDG) uptake values in lumbar spine structures on PET/MRI in patients without low back pain. METHODS: Asymptomatic adults receiving whole-body FDG PET/MRI for nononcological indications were prospectively included. Spinal structures of interest were manually annotated at each lumbar level. Maximum standardized uptake value (SUVmax) and mean SUV (SUVmean) values were extracted from ordered subset expectation maximization, block sequential regularized expectation maximization (BSREM) β450, and BSREM β300 images. The distributions of SUVmax and SUVmean values were evaluated across spinal levels and structures. We also evaluated normalization of SUV metrics with the liver and subcutaneous fat uptake values, and correlations between SUV metrics and BMI, age, and blood glucose level. RESULTS: Twenty-two patients were included for analysis. Average SUVmax on OSEM reconstructions was 2.99 (SD: 0.69) for the vertebral body and 1.95 (SD: 0.54) for the intervertebral disc. In the nervous system, SUVmax for articular, peri-articular, and ligamentous structures was lower, ranging between 1.26 and 1.62. Uptake values were significantly lower on BSREM β450 and higher on BSREM β300. There was a positive correlation between uptake values and BMI, but no correlation was found with age or blood glucose level. CONCLUSION: Normal uptake values in the lumbar spine on [18F]FDG PET/MRI in asymptomatic adults were assessed. The uptake values reported in this study could serve as a reference for the identification of abnormalities in patients with chronic low back pain, but the applied PET reconstruction method and patient BMI should be taken into account.

  PublisherDOI

• Figure S2 from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <p>Top 100 frequent genetic mutations in realworld lung cancer patients</p>

  PublisherDOI

• Figure S7 from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <p>Ascl1 expression across reprocessed RNA-seq datasets</p>

  PublisherDOI

• Perinatal Exposure to Metal Mixtures Disrupts Neuronal Function and Behavior

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-11

  preprintOpen access

  Abstract Background Environmental exposure to heavy metals such as lead (Pb), arsenic (As), hexavalent chromium [Cr(VI)], and cadmium (Cd), (PACC), is linked to neurodevelopmental disorders. These metals often co-occur in contaminated environments, but their combined effects on brain development remain poorly understood. Objective To test the hypothesis that perinatal exposure to a mixture of environmentally relevant levels of Pb, As, Cd, and Cr(VI), causes developmental defects in cognition, behavior, and neuronal function. Methods Female C57BL/6J mice were exposed to either a single metal or the PACC mixture in drinking water. Exposure began two weeks preconception and continued until weaning at postnatal day 21. Juvenile mice were tested at 4–5 weeks of age in open field (locomotion), novel object recognition (short-term memory), Y-maze (working memory), and elevated plus maze (anxiety-like behavior). A subset of animals underwent Whole-cell patch-clamp recordings in the medial prefrontal cortex (mPFC) and hippocampal CA1 neurons. Results Perinatal exposure to PACC metal mixture increased anxiety-like behavior and impaired short-term memory but not locomotion or working memory. Pyramidal neurons in mPFC and hippocampal CA1 displayed increased intrinsic excitability, mPFC neurons also showed elevated amplitude in spontaneous excitatory postsynaptic currents. Discussion Our findings suggest that perinatal exposure to the PACC metal mixture impairs cognition, increases anxiety-like behavior, and alters neuronal function in specific brain regions of juvenile mice, leading to disruption in neuronal function and behavior later in life. Further studies are needed to provide mechanistic insight into how perinatal heavy metal exposure affects neuronal development. Highlights Perinatal exposure to a metal mixture including lead (Pb), arsenic (As), hexavalent chromium (Cr(VI)), and cadmium (Cd), collectively termed PACC metal mixture—impairs cognition and increases anxiety in mice. Neuronal excitability and synaptic transmission are altered in medial prefrontal cortex after PACC metal mixture exposure. PACC mixture exposure decreases short-term memory in both males and females, and increases anxiety in males Principal component and clustering analyses reveal that PACC mixture exposure and control mice form distinct, nonoverlapping populations in physiological-behavioral space. Environmentally relevant PACC metal mixtures exert stronger effects than individual metals alone.

  PublisherOA PDFDOI

• Data from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <div>Abstract<p>Lung cancer, the leading cause of cancer mortality, exhibits diverse histologic subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. In this study, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMM), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCAMGDB aligned 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in the GEMMs. To accompany this resource, a web application was developed that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCAMGDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance.</p><p><b>Significance:</b> The Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB) provides a comprehensive and accessible resource for the research community to investigate lung cancer biology in mouse models.</p></div>

  PublisherDOI

• Table S3 from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <p>Additional datasets, to be included in the next update of LCMAGDB</p>

  PublisherDOI

• Table S7 from The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  2025-05-15

  preprintOpen access

  <p>Genotype table, harmonized genotype descriptions</p>

  PublisherDOI

• The Lung Cancer Autochthonous Model Gene Expression Database Enables Cross-Study Comparisons of the Transcriptomic Landscapes Across Mouse Models

  Cancer Research · 2025-04-29 · 1 citations

  articleOpen access

  Lung cancer, the leading cause of cancer mortality, exhibits diverse histologic subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. In this study, we established the Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMM), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCAMGDB aligned 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in the GEMMs. To accompany this resource, a web application was developed that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCAMGDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance. Significance: The Lung Cancer Autochthonous Model Gene Expression Database (LCAMGDB) provides a comprehensive and accessible resource for the research community to investigate lung cancer biology in mouse models.

  PublisherOA PDFDOI

Frequent coauthors

• Daehyun Yoon

  University of California, San Francisco

  29 shared

• Sanjiv S. Gambhir

  Stanford University

  26 shared

• Peter Cipriano

  Stanford University

  21 shared

• Deepak Behera

  CellSight Technologies (United States)

  18 shared

• Ian Carroll

  University of Freiburg

  13 shared

• Bin Shen

  12 shared

• Sheen‐Woo Lee

  Catholic University of Korea

  12 shared

• Stuart B. Goodman

  Stanford University

  11 shared

Education

• M.D.

  Harvard Medical School – Massachusetts Institute of Technology

  1996

• Other

  Columbia University Medical Center

  2001

• Other

  Stanford University

  2001

• Other

  University of California – Los Angeles

  2003

• Other

  University of California – San Diego

  2002