Titre : Tumeurs de la glande sublinguale

Tumeurs de la glande sublinguale : Questions médicales fréquentes

Termes MeSH sélectionnés :

Models, Statistical

Questions fréquentes et termes MeSH associés

Général 1

#1

Erreur lors de la génération.

Veuillez réessayer ultérieurement.
Sublingual Gland Neoplasms
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Dr Olivier Menir

Contenu validé par Dr Olivier Menir

Expert en Médecine, Optimisation des Parcours de Soins et Révision Médicale


Validation scientifique effectuée le 06/04/2025

Contenu vérifié selon les dernières recommandations médicales

Auteurs principaux

Justin A Bishop

4 publications dans cette catégorie

Affiliations :
  • Department of Pathology, UT Southwestern Medical Center, Dallas, TX.

Shi-Long Zhang

3 publications dans cette catégorie

Affiliations :
  • Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Wuhan University, 430079 Wuhan, China; Dongfeng Stomatological Hospital, Hubei University of Medicine, 442000 Shiyan, China.

Zi-Li Yu

3 publications dans cette catégorie

Affiliations :
  • Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Wuhan University, 430079 Wuhan, China; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, 430079 Wuhan, China. Electronic address: zili09@whu.edu.cn.

Jun Jia

3 publications dans cette catégorie

Affiliations :
  • Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Wuhan University, 430079 Wuhan, China; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, 430079 Wuhan, China. Electronic address: junjia@whu.edu.cn.

Márcio Ajudarte Lopes

2 publications dans cette catégorie

Affiliations :
  • Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas, Piracicaba, Brazil.

Dimitris Tatsis

2 publications dans cette catégorie

Affiliations :
  • Department of Oral and Maxillofacial Surgery, General Hospital of Thessaloniki "G. Papanikolaou", Pylaia.
Publications dans "Tumeurs de la glande sublinguale" :

Dong Hoon Lee

2 publications dans cette catégorie

Affiliations :
  • Department of Otolaryngology-Head and Neck Surgery, 65722Chonnam National University Medical School and Hwasun Hospital, Hwasun, South Korea.
Publications dans "Tumeurs de la glande sublinguale" :

Sang Chul Lim

2 publications dans cette catégorie

Affiliations :
  • Department of Otolaryngology-Head and Neck Surgery, 65722Chonnam National University Medical School and Hwasun Hospital, Hwasun, South Korea.
Publications dans "Tumeurs de la glande sublinguale" :

Si-Rui Ma

2 publications dans cette catégorie

Affiliations :
  • Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Wuhan University, 430079 Wuhan, China; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, 430079 Wuhan, China.
Publications dans "Tumeurs de la glande sublinguale" :

Liang Mao

2 publications dans cette catégorie

Affiliations :
  • Department of Oral and Maxillofacial Surgery, School and Hospital of Stomatology, Wuhan University, 430079 Wuhan, China; State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, 430079 Wuhan, China.
Publications dans "Tumeurs de la glande sublinguale" :

Ilan Weinreb

2 publications dans cette catégorie

Affiliations :
  • Departments of Pathology.

Yoshitaka Utsumi

2 publications dans cette catégorie

Affiliations :
  • Department of Anatomic Pathology, Tokyo Medical University.

Jeffrey Gagan

2 publications dans cette catégorie

Affiliations :
  • Department of Pathology, UT Southwestern Medical Center, Dallas, TX.

Masato Nakaguro

2 publications dans cette catégorie

Affiliations :
  • Department of Pathology and Laboratory Medicine, Nagoya University Hospital, Nagoya, Japan.

Toshitaka Nagao

2 publications dans cette catégorie

Affiliations :
  • Department of Anatomic Pathology, Tokyo Medical University.

Giuseppe Colella

2 publications dans cette catégorie

Affiliations :
  • Multidisciplinary Department of Medical, Surgical and Dental Specialities, Maxillo-Facial Unit, University of Campania "L. Vanvitelli", Naples, Italy.

Leão Pereira Pinto

2 publications dans cette catégorie

Affiliations :
  • Universidade Federal do Rio Grande do Norte - UFRN, Department of Dentistry, Natal, RN, Brazil.

Lélia Batista de Souza

2 publications dans cette catégorie

Affiliations :
  • Universidade Federal do Rio Grande do Norte - UFRN, Department of Dentistry, Natal, RN, Brazil.

M Gontarz

1 publication dans cette catégorie

Affiliations :
  • Department of Cranio-Maxillofacial Surgery Jagiellonian University Medical College, University Hospital 2 Jakubowskiego Street, 30-688 Cracow, Poland michal.gontarz@uj.edu.pl.
Publications dans "Tumeurs de la glande sublinguale" :

M Urbańska-Gąsiorowska

1 publication dans cette catégorie

Publications dans "Tumeurs de la glande sublinguale" :

Sources (10000 au total)

Anatomically Parameterized Statistical Shape Model: Explaining Morphometry Through Statistical Learning.

Statistical shape models (SSMs) are a popular tool to conduct morphological analysis of anatomical structures which is a crucial step in clinical practices. However, shape representations through SSMs... The proposed anatomically parameterized SSM (ANAT[Formula: see text]) is based on learning a linear mapping between shape coefficients (latent space) and selected anatomical parameters (anatomical spa... Anatomical measures of the synthetically generated shapes exhibited realistic statistics. The learned matrices corroborated well with the obtained statistical relationship, while the two SSMs achieved... This study demonstrates the use of anatomical representation for creating anatomically parameterized SSMs and as a result, removes the limited clinical interpretability of standard SSMs.... The proposed models could help analyze differences in relevant bone morphometry between populations, and be integrated in patient-specific pre-surgery planning or in-surgery assessment....

Estimating the Irreducible Uncertainty in Visual Diagnosis: Statistical Modeling of Skill Using Response Models.

For the representative problem of prostate cancer grading, we sought to simultaneously model both the continuous nature of the case spectrum and the decision thresholds of individual pathologists, all... Experts and pathology residents each rated a standardized set of prostate cancer histopathological images on the International Society of Urological Pathologists (ISUP) scale used in clinical practice... The slides were rated by 36 physicians in total: 23 ISUP pathologists and 13 residents. As anticipated, the cases showed a full continuous range of diagnostic severity. Cases ranged along a logit scal... We present a method that allows simultaneous quantification of both the confusability of a particular case and the skill with which raters can distinguish the cases.... The technique generalizes beyond the current example to other clinical situations in which a diagnostician must impose an ordinal rating on a biological spectrum....

Statistical models versus machine learning for competing risks: development and validation of prognostic models.

In health research, several chronic diseases are susceptible to competing risks (CRs). Initially, statistical models (SM) were developed to estimate the cumulative incidence of an event in the presenc... A dataset with 3826 retrospectively collected patients with extremity soft-tissue sarcoma (eSTS) and nine predictors is used to evaluate model-predictive performance in terms of discrimination and cal... Based on the original eSTS data, 100 bootstrapped training datasets are drawn. Performance of the final models is assessed on validation data (left out samples) by employing as measures the Brier scor... Overall, ML techniques are less practical as they require substantial implementation time (data preprocessing, hyperparameter tuning, computational intensity), whereas regression methods can perform w...