Evidence map›Paper›PMID 33511379›Full record

ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2019

Landmark-Guided Deformable Image Registration for Supervised Autonomous Robotic Tumor Resection.

Jiawei Ge, Hamed Saeidi, Justin D Opfermann, Arjun S Joshi, Axel Krieger

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
7.7field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 21 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Supervised Autonomous Electrosurgery for Soft Tissue Resection.Proceedings. IEEE International Symposium on Bioinformatics and Bioengineering · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 1 country.

Jiawei GeDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.ORCID 0000-0001-8336-1837
Hamed SaeidiDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.
Justin D OpfermannSheikh Zayed Institute, Children's National Health System, Washington, DC, USA.
Arjun S JoshiDivision of Otolaryngology - Head and Neck Surgery, George Washington University, Washington, DC, USA.
Axel KriegerDepartment of Mechanical Engineering, University of Maryland, College Park, MD, USA.
University of Maryland, College Park · USChildren's National · USGeorge Washington University · US

Funding

Next Generation of Surgical Imaging and Robotics for Supervised Autonomous Soft Tissue SurgeryR01EB020610 · NIBIB · UNIV OF MARYLAND, COLLEGE PARK · PI HSIEH, MICHAEL HARRISON, KANG, JIN U · 2016 to 2019
$1.5M
Development and Pre-Clinical Evaluation of a Robotic System for Supervised Autonomous Tumor SurgeryR21EB024707 · NIBIB · UNIV OF MARYLAND, COLLEGE PARK · PI KRIEGER, AXEL · 2017 to 2018
$446k
NIBIB NIH HHS R01 EB020610NIBIB NIH HHS R21 EB024707
6 · The paper itself

Abstract

Oral squamous cell carcinoma (OSCC) is the most common cancer in the head and neck region, and is associated with high morbidity and mortality rates. Surgical resection is usually the primary treatment strategy for OSCC, and maintaining effective tumor resection margins is paramount to surgical outcomes. In practice, wide tumor excisions impair post-surgical organ function, while narrow resection margins are associated with tumor recurrence. Identification and tracking of these resection margins remain a challenge because they migrate and shrink from preoperative chemo or radiation therapies, and deform intra-operatively. This paper reports a novel near-infrared (NIR) fluorescent marking and landmark-based deformable image registration (DIR) method to precisely predict deformed margins. The accuracy of DIR predicted resection margins on porcine cadaver tongues is compared with rigid image registration and surgeon's manual prediction. Furthermore, our tracking and registration technique is integrated into a robotic system, and tested using

Indexed as

Deformable image registrationImage-guided surgeryMedical robotics

Identifiers

PMID33511379
PMCPMC7839792
OpenAlexW2979494097

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.