Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs

Mukherjee, Sovanlal; Korfiatis, Panagiotis; Khasawneh, Hala; Rajamohan, Naveen; Patra, Anurima; Suman, Garima; Singh, Aparna; Thakkar, Jay; PG, Nandakumar; Trivedi, Kamaxi H.; Karbhari, Aashna; Chari, Suresh T.; Truty, Mark J.; Halfdanarson, Thorvardur R.; Bolan, Candice W.; Sandrasegaran, Kumar; Majumder, Shounak; Goenka, Ajit H.

doi:10.1016/j.pan.2023.05.008

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…When compared against [ 18 F]FDG PET/CT, [ 68 Ga]FAPI PET/CT was seen to have higher sensitivity for assessing primary tumors, lymph nodes, and distant metastases, which led to upgrades in disease stage and changes in therapeutic strategy. FAPI PET has shown potential to improve the consistency and precision in radiation target volume delineation for PDAC compared to CT [21], which is important in view of high variability in PDAC segmentation on CT due to its infiltrative growth pattern [23][24][25]. FAPI PET also represents an attractive prospect to guide innovative radiation dose painting approaches since there is no physiologic uptake of FAPI in sub-adjacent radiation-sensitive organs such as small bowel.…”

Section: Discussionmentioning

confidence: 99%

Gallium-68-labeled fibroblast activation protein inhibitor-46 PET in patients with resectable or borderline resectable pancreatic ductal adenocarcinoma: A phase 2, multicenter, single arm, open label non-randomized study protocol

Karbhari,

Mosessian,

Trivedi

et al. 2023

PLoS ONE

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Background Pancreatic ductal adenocarcinoma (PDAC) is a lethal disease prone to widespread metastatic dissemination and characterized by a desmoplastic stroma that contributes to poor outcomes. Fibroblast activation protein (FAP)-expressing Cancer-Associated Fibroblasts (CAFs) are crucial components of the tumor stroma, influencing carcinogenesis, fibrosis, tumor growth, metastases, and treatment resistance. Non-invasive tools to profile CAF identity and function are essential for overcoming CAF-mediated therapy resistance, developing innovative targeted therapies, and improved patient outcomes. We present the design of a multicenter phase 2 study (clinicaltrials.gov identifier NCT05262855) of [68Ga]FAPI-46 PET to image FAP-expressing CAFs in resectable or borderline resectable PDAC. Methods We will enroll up to 60 adult treatment-naïve patients with confirmed PDAC. These patients will be eligible for curative surgical resection, either without prior treatment (Cohort 1) or after neoadjuvant therapy (NAT) (Cohort 2). A baseline PET scan will be conducted from the vertex to mid-thighs approximately 15 minutes after administering 5 mCi (±2) of [68Ga]FAPI-46 intravenously. Cohort 2 patients will undergo an additional PET after completing NAT but before surgery. Histopathology and FAP immunohistochemistry (IHC) of initial diagnostic biopsy and resected tumor samples will serve as the truth standards. Primary objective is to assess the sensitivity, specificity, and accuracy of [68Ga]FAPI-46 PET for detecting FAP-expressing CAFs. Secondary objectives will assess predictive values and safety profile validation. Exploratory objectives are comparison of diagnostic performance of [68Ga]FAPI-46 PET to standard-of-care imaging, and comparison of pre- versus post-NAT [68Ga]FAPI-46 PET in Cohort 2. Conclusion To facilitate the clinical translation of [68Ga]FAPI-46 in PDAC, the current study seeks to implement a coherent strategy to mitigate risks and increase the probability of meeting FDA requirements and stakeholder expectations. The findings from this study could potentially serve as a foundation for a New Drug Application to the FDA. Trial registration @ClinicalTrials.gov identifier NCT05262855.

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Section: Discussionmentioning

confidence: 99%

Gallium-68-labeled fibroblast activation protein inhibitor-46 PET in patients with resectable or borderline resectable pancreatic ductal adenocarcinoma: A phase 2, multicenter, single arm, open label non-randomized study protocol

Karbhari,

Mosessian,

Trivedi

et al. 2023

PLoS ONE

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“…Initially, details such as the width and height, slice thickness, and resolution of the DICOM image were extracted from the header of the DICOM file [ 28 , 29 ]. Given that the 3D conversion of DICOM images necessitates exclusively retrieving bone-related image information, a blank matrix was populated with 1 based on the origin coordinate axis, aligning with the Threshold set derived from the Hounsfield Unit (HU) range of 1150–1250.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

Jeon,

Kang,

Kuh

et al. 2023

Diagnostics

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Utilizing “You only look once” (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision–recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis.

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Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques

Huang,

Shen,

Galgano

et al. 2024

Abdom Radiol

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Bounding box-based 3D AI model for user-guided volumetric segmentation of pancreatic ductal adenocarcinoma on standard-of-care CTs

Cited by 8 publications

References 24 publications

Gallium-68-labeled fibroblast activation protein inhibitor-46 PET in patients with resectable or borderline resectable pancreatic ductal adenocarcinoma: A phase 2, multicenter, single arm, open label non-randomized study protocol

Gallium-68-labeled fibroblast activation protein inhibitor-46 PET in patients with resectable or borderline resectable pancreatic ductal adenocarcinoma: A phase 2, multicenter, single arm, open label non-randomized study protocol

Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images

Advancements in early detection of pancreatic cancer: the role of artificial intelligence and novel imaging techniques

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