Drew A Torigian
VerifiedUniversity of Pennsylvania · Rehabilitation Medicine
Active 1998–2026
About
Drew A Torigian, MD, MA, is a Professor of Radiology at the Hospital of the University of Pennsylvania and holds active staff positions in the Department of Radiology at Presbyterian Medical Center, Pennsylvania Hospital, and the Hospital of the University of Pennsylvania. He serves as the Clinical Director of the Medical Image Processing Group (MIPG) within the Department of Radiology at the University of Pennsylvania Medical Center. Dr. Torigian's educational background includes a BA in Chemistry and Mathematics and an MA in Mathematics from Johns Hopkins University, completed in 1991, and an MD from New York University School of Medicine in 1996. His research expertise encompasses oncologic imaging, including lung cancer, melanoma, lymphoma, gastrointestinal, genitourinary, and gynecologic cancers, as well as inflammation imaging, aging imaging, and translational research imaging. He specializes in thoracoabdominopelvic imaging, CT, MRI, PET, PET/CT, and PET/MR imaging, with a focus on functional and molecular imaging, quantitative imaging, and all types of imaging response assessment on CT, MRI, and PET. Dr. Torigian's clinical expertise includes thoracic imaging, body CT and MRI, PET and PET/CT imaging, oncologic imaging, and vascular imaging using CT and MRI techniques.
Research topics
- Artificial Intelligence
- Computer Science
- Pathology
- Anatomy
- Medicine
Selected publications
Evolving Landscape of Chest Wall Reconstruction
Journal of Thoracic Imaging · 2026-01-21
articleCorrespondingChest wall reconstruction (CWR) is a complex and evolving field that clinically benefits from the use of multimodal radiologic imaging. This review summarizes the essential role of multimodal imaging, such as ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET), in preoperative and postoperative CWR evaluation. Preoperative CWR planning involves characterization of defects, assessment of surrounding structures, and guidance for surgical approach and implant selection. Postoperative CWR evaluation focuses on monitoring graft/flap viability, assessing structural integrity, and identifying complications such as infection or hardware failure. This article guided radiologists in approaching CWR cases and creating effective reports to guide patient management.
2026-04-01
articleCancer Research · 2026-04-03
articleAbstract Introduction: Complete responses to PARP inhibitor (PARPi) monotherapy in recurrent high-grade serous ovarian cancer (HGSOC) are rare. However, preclinical data have demonstrated promising synergy between PARP and ATR inhibitors. Characterizing the immune contexture of the tumor microenvironment and the surrounding stroma during treatment may provide valuable biological insights into the efficacy of this combination therapy and inform future combinations. Methods: Patients with recurrent HGSOC received ceralasertib 160mg orally daily, days 1-7 and olaparib 300mg twice daily, days 1-28 of a 28-day cycle. 18 tissue samples were collected across archival (resection) and pre-treatment and on-treatment timepoints (core biopsies). Each sample was analyzed using a 25-plex multiplex immunohistochemistry (mIHC) assay, which interrogates cell composition and functional states of neoplastic and immune cell types, including all major lymphoid and myeloid populations. Segmented cells were assigned to either a tumor or stroma compartment using a PanCK mask that was uniformly expanded by 25μm, and average cell densities were calculated for each compartment. Results: Samples obtained during combination PARPi + ATRi treatment demonstrated widespread increases in immune cell densities including T cells (CD8+, Tregs, and Th1-like cells), B cells, dendritic cells, macrophages, and monocytes. Among the T-cell populations, higher densities of Granzyme B and PD-1 were observed, indicating enhanced cytotoxic activity and immune engagement. Concurrently, there was a decrease in proliferating neoplastic cells (PanCK+Ki67+), consistent with reduced tumor cell proliferation during treatment. Using the PanCK tumor mask, we observed that CD8+ T cells, Th1-like cells, B cells, and dendritic cells increased more prominently within the tumor compartment compared to the surrounding stroma. Samples obtained prior to treatment from patients with stable or progressive disease (SD/PD) exhibited higher macrophage densities, primarily attributable to elevated levels of M2-like (immunosuppressive) macrophages. Conclusions: The increased immune cell densities measured by mIHC indicate overall activation of the immune system following PARPi + ATRi treatment. Elevated levels of PD-1+ and Granzyme B+ T cells suggest enhanced immune activation and cytotoxic potential, while comparative analysis of the tumor versus stroma compartments demonstrates improved immune cell infiltration into the tumor. Notably, higher baseline densities of M2-like macrophages may influence or limit response to therapy. Collectively, these findings provide evidence that PARPi + ATRi combination therapy promotes anti-tumor immune activity. However, additional data is needed to correlate these immune changes with clinical outcomes. Citation Format: Elias Pavlatos, Benjamin Tate, Austin Nguyen, Ian S. Heller, Dimitrios Nasioudis, Janos L. Tanyi, Drew A. Torigian, Diego Rodriguez, Susan M. Domchek, Ronny I. Drapkin, Eric J. Brown, Gordon B. Mills, Fiona Simpkins. Increased immune activity in patients with high-grade serious ovarian cancer after combination PARPi + ATRi therapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 7749.
2026-04-01
articleWe present a technical framework for integrating breathing frequency into regional volumetric analysis of free-breathing 4D dynamic MRI in pediatric populations. Regional quantitative MRI endpoints traditionally focused on tidal volume amplitudes but did not capture breathing rate, which varies substantially with age and respiratory state. We describe implementation of frequency normalized regional metrics (RR/TV ratios) alongside standard regional volumetric measures, using Z-score standardization against age-matched reference data. This approach is demonstrated in 47 pediatric thoracic insufficiency syndrome (TIS) patients with paired pre/post-VEPTR surgery scans, compared to 200 healthy controls. Technical components include respiratory rate derived during 4D reconstruction via the OFx method, regional volumetry of lung compartments, and reference space normalization using the Virtual Growing Child (VGC) normative database. We show that frequency weighted outcomes exhibit stronger developmental trends (Spearman ρ = -0.54 for lung RR/TV vs. +0.40 to +0.44 for lung volumes) and larger effect sizes when tracking surgical changes (r = 0.54–0.64 for RR/TV vs. r = 0.66–0.83 for TV). The framework can be integrated into existing 4D-MRI pipelines with minimal overhead and provide a more complete characterization of breathing mechanics in pediatric chest wall disorders.
Predicting Risk of Pulmonary Fibrosis Formation in PASC Patients
2025-08-07
articleWhile the acute phase of the COVID-19 pandemic has subsided, its long-term effects persist through Post-Acute Sequelae of COVID-19 (PASC), commonly known as Long COVID. There remains substantial uncertainty regarding both its duration and optimal management strategies. PASC manifests as a diverse array of persistent or newly emerging symptoms—ranging from fatigue, dyspnea, and neurologic impairments (e.g., brain fog), to cardiovascular, pulmonary, and musculoskeletal abnormalities—that extend beyond the acute infection phase. This heterogeneous presentation poses substantial challenges for clinical assessment, diagnosis, and treatment planning. In this paper, we focus on imaging findings that may suggest fibrotic damage in the lungs, a critical manifestation characterized by scarring of lung tissue that may impair long-term respiratory function in patients with PASC. This study introduces a novel multi-center chest CT analysis framework that combines deep learning and radiomics for fibrosis prediction. Our approach leverages convolutional neural networks (CNNs) and interpretable feature extraction, achieving 82.21% accuracy and 85.46% AUC in classification tasks. We demonstrate the effectiveness of Grad-CAM visualization and radiomics-based feature analysis in providing clinically relevant insights for PASC-related lung fibrosis prediction. These findings demonstrate the potential of deep learning-driven computational methods in enabling early detection and risk assessment of PASC-related lung fibrosis—presented for the first time in the literature.
2025-11-25
articleOpen access<p>Clinical response correlates.</p>
2025-11-25
articleOpen access<p>Trial schema</p>
2025-11-25
articleOpen access<p>18F]FTT-PET on subject previously treated with PARPi.</p>
Blood · 2025-11-03
articleOpen accessAbstract Introduction: We previouslyreportedthe feasibility of predicting treatment outcome of chimeric antigen receptor modified T-cell (CAR-T) therapy for patients (pts) with relapsed or refractory large B-cell lymphomas (r/r LBCL) from pretreatment diagnostic imaging studies. This method used deep-learning (DL)-based image analysis for lesion-level response prediction and estimated patient-level outcomes from lesion-level predictions by rule-based reasoning (Tong Y, et al. PLoS ONE 2023;18(7):e0282573). To further test the prognostic validity of this method, we analyzed baseline (pre-CAR-T infusion) FDG-PET (PET) and low-dose CT (LD-CT) images performed as part of the JULIET trial with investigators blinded to pt treatment outcomes. The JULIET trial is a phase 2 study of tisagenlecleucel, a CD19-directed CAR-T therapy, in adult pts with r/r LBCL (NCT02445248). Here, we compare this approach with serum LDH and secondary International Prognostic Index (sIPI), which are generally accepted prognostic markers for LBCL treatment outcome. Methods: Pre-infusion imagesfrom 102 adult pts with r/r LBCL who were treated with tisagenlecleucel were evaluated. Image sets came from 27 hospitals in 10 countries and were acquired on 15 different model scanners from 3 leading manufacturers of diagnostic imaging equipment; 36 (35%) pt image sets were excluded from DL-based image analysis: 31 (30%) due to image low quality (26 LD-CT; 4 LD-CT+PET; 1 PET), 1 without nodal lesion, and 4 without metabolic confirmation of Month-12 response. Data from 3 contiguous whole-image slices through the mid-portions of nodal lesions on both PET and LD-CT images from each of the 66 pt image sets were analyzed using the previously described DL lesion-level model, without retraining, to predict treatment outcome. After analyzing image sets to generate predictions of outcome for each of the 66 evaluable pts, actual Month-12 pt outcomes were unblinded and grouped using protocol-specified radiologic response criteria as a Responder (met protocol-defined radiologic complete response [CR]) or a Non-responder (met protocol-defined radiologic partial response, stable disease or progressive disease [&lt; CR]) at 12 months post CAR-T infusion. Actual Month-12 pt outcomes post-treatment (verified Responders, n = 13 [i.e., 20% CR]; verified Non-responders, n = 53 [i.e., 80% &lt; CR]) were then compared with predictions obtained in blinded fashion from DL-based image analyses using 70% rule-based reasoning (i.e., if &gt; 70% of lesions were predicted to respond by DL image analysis, the pt was predicted to be a Responder at Month-12; if &lt; 70% lesions were predicted to respond, the pt was predicted to be a Non-responder at Month-12). Results: For 66 evaluable pts, DL-based prediction of Responder (CR) status as the outcome at Month-12 had a sensitivity of 77% (correctly identified Responders) with specificity 51% and balanced accuracy 64% (balanced accuracy reported because of imbalance between number of Responders and non-Responders). For comparison, serum LDH and sIPI score at enrollment were also evaluated as prognostic indices for 64 pts (2/66 pts excluded for missing LDH). Using serum LDH &lt; 2 x upper limit of normal (ULN) to predict the outcome at Month-12 as Responder, sensitivity was 100% but specificity only 12% due to a high false positive rate; using LDH &gt; 2 x ULN to predict outcome as Non-responder, sensitivity was only 12% (high false negative rate) with specificity 100%. Using sIPI &gt; 2 to predict outcome as Non-responder at 12 months, sensitivity was 65% and specificity 62%. Conclusions: Prediction of CAR-T treatment outcome from pretreatment images using DL-based image analysis for lesion-level response prediction and rule-based reasoning for patient-level response estimation is feasible. Considering the challenges stemming from data heterogeneity and the small number of pts in this study, this approach showed a generalizable performance with the accuracy of patient-level predictions similar to earlier results obtained from our single center study. With continued refinement and addition of clinical covariates, this approach has the potential to provide clinically useful information in advance of CAR-T therapy.
Gynecologic Oncology · 2025-09-01
article
Recent grants
NIH · $2.3M · 2021–2026
Virtual growing child 5-dimensional functional models for treating respiratory anomalies
NIH · $2.3M · 2020–2024
Virtual growing child 5-dimensional functional models for treating respiratory anomalies
NIH · $800k · 2020–2023
Frequent coauthors
- 226 shared
Abass Alavi
Hospital of the University of Pennsylvania
- 162 shared
Jayaram K. Udupa
University of Pennsylvania
- 119 shared
Yubing Tong
California University of Pennsylvania
- 103 shared
János L. Tanyi
Cancer Research Center
- 96 shared
Babak Saboury
National Institutes of Health Clinical Center
- 61 shared
Gregory L. Beatty
University of Pennsylvania
- 58 shared
Fiona Simpkins
University of Pennsylvania Health System
- 58 shared
Nawar Latif
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