Editorial: The role of imaging in gynecological malignancies
Notice bibliographique
Résumé
Despite the improvements recorded in the last decades, the outcome of patients with gynecological cancer is still unfavorable, in particular for some tumors and for some risk groups. At the same time, the evolution of imaging techniques in oncology is progressively accelerating, with the growing importance of topics such as the application of artificial intelligence and the study of body composition. The papers included in this special article collection present an interesting summary of novel and still controversial aspects in the field of imaging of gynecological tumors (Scepanovic et al.; Hu et al.; Wang J. et al.; Matsuura et al.; Zhu et al.; Rizzo et al.; Cordoba et al.; Matani et al.; deSouza et al.; Su et al.; Wang C. et al.; Ciulla et al.; Jeon et al.; Ambrosio et al.; Wang X. et al.; Bi et al.; Shao et al.; Han et al.; Turco et al.). As evidence of the evolving scenario, it is particularly interesting to note, in this special collection, the lack of "classic" studies based on the evaluation of the diagnostic performance of single methods or on the comparison between different imaging methods. Some studies evaluated the accuracy of imaging in the non-invasive prediction of malignancy in suspicious lesions of the female reproductive organs. In particular, it has been observed that: i) diffusion-weighted MRI (Scepanovic et al.) and multi-parametric (mp) MRI-based radiomic image analysis (Bi et al.) are able to effectively differentiate benign endometrial masses from malignant ones ; ii) the US-based ADNEX model can differentiate benign adnexal masses from malignant or borderline ones (Hu et al.); iii) mpMRI can distinguish uterine sarcomas from leiomyomas and atypical leiomyomas (Wang C. et al.); IV) 3T MRI can predict histological type and grading in cervical carcinomas (Shao et al.). Conversely, mpMRI was unable to predict grading in endometrial tumors (Scepanovic et al.).In terms of imaging-based prediction of tumor response, one study showed that superb microvascular imaging (an US image processing method) predicts response to chemoradiation in patients with locally advanced cervical cancer (Zhu et al.), while a literature review achieved similar conclusions regarding MRI in the same setting (Matani et al.). Other studies reported the possibility of predicting outcome by imaging in gynecological malignancies. In particular, it has been observed that radiomics analysis of CT and MRI images can predict recurrences in locally advanced cervical cancer (Wang J. et al.) and that similar results can be achieved, in the same patients, through MRI-based tumor monitoring during treatment (Cordoba et al.), as confirmed by a literature review on the role of MRI in cervical cancers (Matani et al.). Also, the increase in abdominal fat in sarcopenic patients (assessed by CT) during treatment is correlated with a greater risk of recurrences (Han et al.). Conversely, tumor size estimation by US did not correlate with prognosis in endometrial carcinomas (Ambrosio et al.).Other studies reported on imaging assessments of tumor biology. In particular, it was demonstrated that DCE-MRI is able to effectively evaluate the degree of proliferation (Ki-67) in cervical tumors (Wang X. et al.), while a literature review analyzed the role of imaging in evaluating the degree of hypoxia in endometrial tumors (deSouza et al.). Interestingly, two papers reported on the role of imaging in predicting or analyzing anticancer drug toxicity. In particular, a systematic review found variable association of body composition by imaging and chemo-induced toxicity in ovarian tumors (Rizzo et al.) while, in a case report, the authors observed that MRI can help distinguish, between two drugs, which one is responsible for the neurological toxicity (Matsuura et al.).Instead, other studies evaluated the results of integrating different imaging methods. One paper reported a higher sensitivity and specificity, in discriminating adnexal masses, in case of combination of ADNEX model (US) and MRI (Hu et al.), while another study showed better results in the differential diagnosis between uterine sarcomas and benign lesions with the combination of conventional MRI and diffusion-weighted MRI (Wang C. et al.). In addition, a paper reported effective risk stratification, in patients with cervical cancer, by radiomics analysis based on CT and MRI (Wang J. et al.). Finally, one study showed that, compared to a single method, the combined CT-based assessment of both waist skeletal muscle volume using an AI-based tool and waist fat gained during treatment improves the prediction of outcomes (Han et al.).Other studies reported on dynamic imaging assessments. A paper reported reliable prediction of tumor response with US-based superb microvascular imaging monitoring in patients with locally advanced cervical cancer treated with chemoradiation (Zhu et al.). Furthermore, in the same setting, a study showed that tumor monitoring by MRI allows an effective prediction of disease-free survival and overall survival (Cordoba et al.), as confirmed by a review of the literature (Matani et al.). Finally, another literature review on advanced cervical cancer discussed the role of different imaging methods (spectroscopy, PET-MRI, radiomics) in tumor monitoring during radiotherapy and in assessing clinical response (Ciulla et al.).As further evidence of the growing importance of imaging in the clinical management of patients with gynecological cancers, we must underline the importance of a study evaluating the impact of 18 FDG-PET-CT in 4167 patients with IB-IVA cervical cancer treated with radiotherapy or chemoradiation. The study, which categorized patients (propensity score-matching) based on whether or not 18 FDG-PET-CT was performed, showed, regardless of tumor stage, a significant improvement in overall survival in patients undergoing PET (HR: 0.88; 95%CI: 0.80-0.97; p=0. ) represent a useful update for researchers and health professionals involved in gynecological cancers. Furthermore, we hope that these papers will be a starting point and a stimulus for the design and conduct of further clinical studies in this field. Finally, the growing role of imaging not only in the diagnosis and staging of gynecological cancers, but also in the prediction of the prognosis or even in its refinement, suggest the need for an ever greater integration of imaging in the multidisciplinary management of these neoplasms.
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 | 0,004 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,005 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,005 | 0,001 |
| Intégrité de la recherche | 0,012 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,011 |
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 ».