Differentiating Malignant and Healthy Areas in Isolated Kidney Samples Through Infrared Visualization Techniques
Notice bibliographique
Résumé
Background: Because partial nephrectomy (PN) may remove malignant tissue while maintaining kidney function, it is currently the gold standard for nephrectomy. However, the blood arteries that supply the kidney are clamped at the start of the procedure. The most common method for evaluating surgical margins during PN is intraoperative frozen section (FS) evaluation. Its long duration and high false-negative rate question its reliability and efficacy. This encouraged us to search for a much quicker and easier method. Methods: The infrared (IR) imaging approach uses the differences in optical density between tumor and healthy tissue to create the sharp contrast in the IR images. The cancerous kidneys were examined after a radical nephrectomy. Following the removal of the cancerous tissue and some of the surrounding healthy tissue, the samples were examined using the IR method. For the IR analysis, we created specific software. Following that, tissue samples taken from both healthy and malignant areas were subjected to a histomorphological analysis. Results: Experiments showed that malignant tissue appeared as areas of high blackness in the IR picture, while healthy tissue appeared as areas of high illumination. Our software highlighted the areas of the IR image that were associated with the healthy and malignant portions, computed their average brightness, and calculated the ratio of the average illumination (RAI) of the malignant area to that of the healthy area. RAI is an interval of numbers obtained as a result of dividing the average brightness of all dark areas in all examined samples by all light areas of all examined samples. The 95% probability interval for RAIs taking place, which ranged from 0.25 to 0.41, was calculated. The location of the malignancy was then identified by a histomorphological examination. The compliance between histomorphological results and the outcomes of IR examination was confirmed in all cases. Conclusions: The IR imaging technique offers significant promise for improving the accuracy and efficiency of margin assessment during kidney cancer surgeries. The IR imaging technique can provide immediate feedback on the tumor boundaries, which could potentially reduce the duration of warm ischemia during surgery. Subsequent investigations should be focused on verifying the technology in further clinical trials and investigating its integration into the surgical process, which could result in its acceptance as a standard instrument for intraoperative decision-making in kidney cancer operations.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».