About

Chirag Patel is a Clinical Assistant Professor in the Radiology - Rad/Molecular Imaging Program at Stanford University. He is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI) at Stanford. His work focuses on the application of artificial intelligence in medicine and imaging, contributing to research and education in these fields. As part of his role, he is involved in advancing AI-driven healthcare solutions and fostering collaborations between academia and industry to improve medical imaging and diagnostics.

Research topics

• Medicine
• Computer Science
• Internal medicine
• Artificial Intelligence
• Data Mining
• Machine Learning
• Political Science
• Pathology
• Business
• Biology
• Cancer research
• Immunology
• Algorithm
• Radiology
• Engineering
• Engineering ethics
• Medical education

Selected publications

• TNTdetect.AI: A Deep Learning Model for Automated Detection and Counting of Tunneling Nanotubes in Microscopy Images

  Cancers · 2022 · 7 citations

  Senior authorCorresponding
  • Artificial Intelligence
  • Computer Science
  • Artificial Intelligence

  BACKGROUND: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. METHODS: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions of images with TNTs by implementing a classification algorithm. Second, we fed parts of the image classified as TNT-containing into a modified U-Net model to estimate TNTs on a pixel-wise basis. RESULTS: = 0.78). CONCLUSIONS: Our automated approach labeled and detected TNTs and cells imaged in culture, resulting in comparable TCRs to those determined by human experts. Future studies will aim to improve on the accuracy, precision, and recall of the algorithm.

  PublisherOA PDFDOI

• Increasing Diversity in Radiology and Molecular Imaging: Current Challenges

  Molecular Imaging and Biology · 2021 · 12 citations

  • Political Science
  • Medical education
  • Medicine
  PublisherOA PDFDOI

• Comparison of Multiparametric Magnetic Resonance Imaging–Targeted Biopsy With Systematic Transrectal Ultrasonography Biopsy for Biopsy-Naive Men at Risk for Prostate Cancer

  JAMA Oncology · 2021 · 196 citations

  • Medicine
  • Radiology
  • Internal medicine

  Importance: Magnetic resonance imaging (MRI) with targeted biopsy is an appealing alternative to systematic 12-core transrectal ultrasonography (TRUS) biopsy for prostate cancer diagnosis, but has yet to be widely adopted. Objective: To determine whether MRI with only targeted biopsy was noninferior to systematic TRUS biopsies in the detection of International Society of Urological Pathology grade group (GG) 2 or greater prostate cancer. Design, Setting, and Participants: This multicenter, prospective randomized clinical trial was conducted in 5 Canadian academic health sciences centers between January 2017 and November 2019, and data were analyzed between January and March 2020. Participants included biopsy-naive men with a clinical suspicion of prostate cancer who were advised to undergo a prostate biopsy. Clinical suspicion was defined as a 5% or greater chance of GG2 or greater prostate cancer using the Prostate Cancer Prevention Trial Risk Calculator, version 2. Additional criteria were serum prostate-specific antigen levels of 20 ng/mL or less (to convert to micrograms per liter, multiply by 1) and no contraindication to MRI. Interventions: Magnetic resonance imaging-targeted biopsy (MRI-TB) only if a lesion with a Prostate Imaging Reporting and Data System (PI-RADS), v 2.0, score of 3 or greater was identified vs 12-core systematic TRUS biopsy. Main Outcome and Measures: The proportion of men with a diagnosis of GG2 or greater cancer. Secondary outcomes included the proportion who received a diagnosis of GG1 prostate cancer; GG3 or greater cancer; no significant cancer but subsequent positive MRI results and/or GG2 or greater cancer detected on a repeated biopsy by 2 years; and adverse events. Results: The intention-to-treat population comprised 453 patients (367 [81.0%] White, 19 [4.2%] African Canadian, 32 [7.1%] Asian, and 10 [2.2%] Hispanic) who were randomized to undergo TRUS biopsy (226 [49.9%]) or MRI-TB (227 [51.1%]), of which 421 (93.0%) were evaluable per protocol. A lesion with a PI-RADS score of 3 or greater was detected in 138 of 221 men (62.4%) who underwent MRI, with 26 (12.1%), 82 (38.1%), and 30 (14.0%) having maximum PI-RADS scores of 3, 4, and 5, respectively. Eighty-three of 221 men who underwent MRI-TB (37%) had a negative MRI result and avoided biopsy. Cancers GG2 and greater were identified in 67 of 225 men (30%) who underwent TRUS biopsy vs 79 of 227 (35%) allocated to MRI-TB (absolute difference, 5%, 97.5% 1-sided CI, -3.4% to ∞; noninferiority margin, -5%). Adverse events were less common in the MRI-TB arm. Grade group 1 cancer detection was reduced by more than half in the MRI arm (from 22% to 10%; risk difference, -11.6%; 95% CI, -18.2% to -4.9%). Conclusions and Relevance: Magnetic resonance imaging followed by selected targeted biopsy is noninferior to initial systematic biopsy in men at risk for prostate cancer in detecting GG2 or greater cancers. Trial Registration: ClinicalTrials.gov Identifier: NCT02936258.

  DOI

• Intravital imaging reveals synergistic effect of CAR T-cells and radiation therapy in a preclinical immunocompetent glioblastoma model

  OncoImmunology · 2020 · 91 citations

  • Medicine
  • Cancer research
  • Immunology

  Recent advances in novel immune strategies, particularly chimeric antigen receptor (CAR)-bearing T-cells, have shown limited efficacy against glioblastoma (GBM) in clinical trials. We currently have an incomplete understanding of how these emerging therapies integrate with the current standard of care, specifically radiation therapy (RT). Additionally, there is an insufficient number of preclinical studies monitoring these therapies with high spatiotemporal resolution. To address these limitations, we report the first longitudinal fluorescence-based intravital microscopy imaging of CAR T-cells within an orthotopic GBM preclinical model to illustrate the necessity of RT for complete therapeutic response. Additionally, we detail the first usage of murine-derived CAR T-cells targeting the disialoganglioside GD2 in an immunocompetent tumor model. Cell culture assays demonstrated substantial GD2 CAR T-cell-mediated killing of murine GBM cell lines SB28 and GL26 induced to overexpress GD2. Complete antitumor response in advanced syngeneic orthotopic models of GBM was achieved only when a single intravenous dose of GD2 CAR T-cells was following either sub-lethal whole-body irradiation or focal RT. Intravital microscopy imaging successfully visualized CAR T-cell homing and T-cell mediated apoptosis of tumor cells in real-time within the tumor stroma. Findings indicate that RT allows for rapid CAR T-cell extravasation from the vasculature and expansion within the tumor microenvironment, leading to a more robust and lasting immunologic response. These exciting results highlight potential opportunities to improve intravenous adoptive T-cell administration in the treatment of GBM through concurrent RT. Additionally, they emphasize the need for advancements in immunotherapeutic homing to and extravasation through the tumor microenvironment.

  DOI

Frequent coauthors

• Sanjiv S. Gambhir

  Stanford University

  59 shared

• Corinne Beinat

  Stanford University

  45 shared

• Surya Murty

  33 shared

• Edwin Chang

  Stanford University

  31 shared

• Renuka Erande

  Campbell Collaboration

  30 shared

• Tom Haywood

  29 shared

• David Bruce

  29 shared

• Lewis Naya

  Stanford Cancer Institute

  28 shared

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