Dr. Michael I. I. D’Angelica Profile

Dr. Michael I. I. D’Angelica

at Memorial Sloan Kettering Cancer Ctr

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Author

Publications (4)

SPIE Journal Paper | 4 March 2024

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation

Bowen Xiang, Jon Heiselman, Winona Richey, Michael D’Angelica, Alice Wei, T. Peter Kingham, Frankangel Servin, Kyvia Pereira, Sunil Geevarghese, William Jarnagin, Michael Miga

JMI, Vol. 11, Issue 02, 025001, (March 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.025001

KEYWORDS: Liver, Natural surfaces, Deformation, Image registration, Surgery, Data acquisition, Data modeling, Rigid registration, Optical tracking, Image segmentation

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PurposeTo study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery.ApproachA Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation.ResultsSurface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was −1.05 mm and using NRM-VC was −1.42 mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively.ConclusionThe first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

Proceedings Article | 4 April 2022 Presentation + Paper

CT radiomics to predict early hepatic recurrence after resection for intrahepatic cholangiocarcinoma

Jayasree Chakraborty, Joshua Jolissaint, Tiegong Wang, Kevin Soares, Mithat Gonen, Linda Pak, Thomas Boerner, Richard K. Do, Vinod Balachandran, Michael D'Angelica, Jeffrey Drebin, T. Peter Kingham, Alice Wei, William Jarnagin

Proceedings Volume 12033, 120333C (2022) https://doi.org/10.1117/12.2612889

KEYWORDS: Tumors, Surgery, Liver, Computed tomography, Liver cancer, Feature extraction, Cancer, Oncology

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SPIE Journal Paper | 25 June 2020

Multimodal radiomics and cyst fluid inflammatory markers model to predict preoperative risk in intraductal papillary mucinous neoplasms

Kate Harrington, Travis Williams, Sharon Lawrence, Jayasree Chakraborty, Mohammad A. Al Efishat, Marc Attiyeh, Gokce Askan, Yuting Chou, Alessandra Pulvirenti, Caitlin McIntyre, Mithat Gönen, Olca Basturk, Vinod Balachandran, T. Peter Kingham, Michael D'Angelica, William Jarnagin, Jeffrey Drebin, Richard K. Do, Peter Allen, Amber Simpson

JMI, Vol. 7, Issue 03, 031507, (June 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.3.031507

KEYWORDS: Computed tomography, Data modeling, Pancreatic cancer, Tumors, Surgery, Proteins, Performance modeling, Tumor growth modeling, Cancer, Feature extraction

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Proceedings Article | 16 March 2020 Presentation + Paper

A combined radiomics and cyst fluid inflammatory markers model to predict preoperative risk in pancreatic cystic lesions

Travis Williams, Kate Harrington, Sharon Lawrence, Jayasree Chakraborty, Mohammad Al Efishat, Marc Attiyeh, Gokce Askan, Yuting Chou, Alessandra Pulvirenti, Caitlin McIntyre, Mithat Gonen, Olca Basturk, Vinod Balachandran, T. Peter Kingham, Michael D'Angelica, Williarm Jarnagin, Jeffrey Drebin, Richard K. Do, Peter Allen, Amber Simpson

Proceedings Volume 11315, 113151Q (2020) https://doi.org/10.1117/12.2566425

KEYWORDS: Pancreatic cancer, Computed tomography, Tumor growth modeling, Cancer, Feature extraction, Proteins, Tumors, Data modeling, Performance modeling, Pancreas

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