Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Editorial: 10th anniversary of frontiers in surgery: celebrating progress and envisioning the future of multidisciplinary surgery.

Frontiers in surgery·2026
Same author

Reconstruction of Lower Lip-Neck Contracture Using Skin Graft and Circumflex Scapular Artery-Augmented Occipito-cervico-dorsal Flap.

Plastic and reconstructive surgery. Global open·2026
Same author

Management of Acute Postoperative Infections Following Microtia Reconstruction With Costal Cartilage Grafts.

Plastic and reconstructive surgery. Global open·2026
Same author

Japanese Consensus Document on NexoBrid<sup>®</sup>, a Burn Eschar Removal Agent.

European burn journal·2026
Same author

A New Surgical Field Expansion Technique in Robot-Assisted Gastrectomy: "Bursal Space Lifting Approach".

Asian journal of endoscopic surgery·2026
Same author

Volumetric Assessment of a Soft-Tissue Tumor Using Smartphone Light Detection and Ranging and Neural Radiance Fields: A Single Case.

Plastic and reconstructive surgery. Global open·2026

Related Experiment Video

Updated: Aug 26, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

180

A Scoring System That Predicts Difficult Lipoma Resection: Logistic Regression and Tenfold Cross-Validation Analysis.

Goh Akiyama1, Shimpei Ono2, Tetsuro Sekine3

  • 1Department of Plastic, Reconstructive and Aesthetic Surgery, Nippon Medical School Hospital, 1-1-5 Sendagi Bunkyo-ku, Tokyo, 113-8603, Japan. s9003@nms.ac.jp.

Dermatology and Therapy
|October 7, 2022
PubMed
Summary

A new scoring system helps predict difficult lipoma resections. Identifying factors like location and unclear boundaries preoperatively can improve surgical planning and outcomes for lipoma removal.

Keywords:
LipomaLogistic regression analysisResectionScoring systemSurgical difficulty

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

346

Related Experiment Videos

Last Updated: Aug 26, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

180
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
06:46

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

346

Area of Science:

  • Surgical Oncology
  • Dermatology
  • Radiology

Background:

  • Lipomas are common benign tumors, typically easy to remove surgically.
  • However, some lipomas present surgical challenges due to their location or characteristics.
  • Preoperative identification of difficult resections is crucial for surgical planning.

Purpose of the Study:

  • To identify clinical and radiological risk factors for difficult lipoma resection.
  • To develop a clinically useful scoring system for predicting preoperative surgical difficulty.

Main Methods:

  • Retrospective analysis of 86 lipoma resection cases (2016-2018).
  • Surgical difficulty defined by tissue separation challenges or inability to remove in one piece.
  • Multivariate logistic regression and Receiver Operating Characteristic (ROC) analysis used to identify predictors and validate a scoring system.

Main Results:

  • 36% of lipoma resections were classified as surgically difficult.
  • Key predictors identified: subfascial intramuscular location, broad contact with structures, in-flowing vessels, and unclear boundaries.
  • A 0-4 point scoring system demonstrated 82.4% accuracy, with scores >= 2 predicting difficulty (55% sensitivity, 98% specificity).

Conclusions:

  • A novel scoring system effectively predicts lipoma resection difficulty.
  • This tool can aid surgeons in preoperative preparation, potentially facilitating smoother surgical procedures.