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Academic literature on the topic 'Thérapie néoadjuvante'
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Journal articles on the topic "Thérapie néoadjuvante"
Mansuet-Lupo, Audrey, Ludovic Fournel, Marie Wislez, Marco Alifano, and Diane Damotte. "Évaluation de la réponse pathologique après thérapie néoadjuvante dans les cancers pulmonaires." Revue Francophone des Laboratoires 2021, no. 528 (January 2021): 43–48. http://dx.doi.org/10.1016/s1773-035x(20)30393-2.
Full textSow, O., NS Fetche, C. Vermare, B. Annabel, A. Anusca, and L. Perrot. "C34: Résultat de la prise en charge des tumeurs stromales gastro-intestinales (GIST) : A propos de 6 cas au centre hospitalier de Vichy (France)." African Journal of Oncology 2, no. 1 Supplement (March 1, 2022): S15. http://dx.doi.org/10.54266/ajo.2.1s.c34.myrhvjbqpy.
Full textKeita, M., A. Toure, I. Camara, A. Barry, M. Koulibaly, and B. Traore. "C98: Impact de la chirurgie associée à la radiothérapie sur le pronostic du cancer du sein en Guinée : Etude cohorte des cancers du sein de Guinée." African Journal of Oncology 2, no. 1 Supplement (March 1, 2022): S41—S42. http://dx.doi.org/10.54266/ajo.2.1s.c98.jdql5963.
Full textJouanneau, E., F. Calvanese, R. Manet, A. Vasiljevic, H. Paris-Refahi, F. Ducray, and G. Raverot. "La thérapie ciblée avec inhibiteurs de BRAF et MEK comme traitement néoadjuvant pour les craniopharyngiomes papillaires des adultes : un nouveau paradigme." Annales d'Endocrinologie 83, no. 5 (October 2022): 314. http://dx.doi.org/10.1016/j.ando.2022.07.101.
Full textDissertations / Theses on the topic "Thérapie néoadjuvante"
Malhaire, Caroline. "Optimization of the Prediction of Complete Response to Neoadjuvant Chemotherapy in Breast Cancer by Breast MRI : Contributions of Semantic Descriptors, Radiomics, and Segmentation Methods." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPAST006.
Full textNeoadjuvant chemotherapy (NAC) has become a cornerstone therapeutic strategy for aggressive and locally advanced breast cancers. Achieving a pathologic complete response (pCR), defined as the absence of residual tumour in both breast and axilla, is associated with improved survival, particularly in triple-negative and HER2-positive breast cancers. This approach allows treatment adjustment according to the tumour response, with escalation therapies shown to enhance survival in patients with incomplete response. Targeted axillary dissection and sentinel lymph node biopsy have emerged as alternatives to axillary lymph node dissection for patients achieving nodal response at the end of NAC. European guidelines currently restrict axillary surgical de-escalation to early-stage, pre-treatment nodal involvement, emphasizing the need for reliable predictive models to guide surgical strategies. MRI plays a critical role in initial assessment for selecting candidates for NAC and evaluating tumour response. This thesis aimed to identify robust and reproducible MRI-based predictors of tumour response that are applicable in clinical practice. Moreover, extracting quantitative MRI data requires precise segmentation of breast tumours, which is labour-intensive and subject to inter-radiologist variability, highlighting the need for automated solutions. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have spurred interest in automated 3D tumour segmentation.Using a database of NAC-treated patients evaluated by MRI, we investigated parameters associated with both pCR and nodal response based on semantic criteria assessed on initial MRI, employing random forest algorithms.We demonstrated that non-spiculated margins and multifocality are independent predictors of tumour response, after adjusting for histomolecular subtype, TILs expression, and T stage of the TNM classification.For patients with initial nodal involvement, we developed a multivariable model to predict complete nodal response, using tumour subtype, KI-67 expression, tumour depth in the breast, intratumoral T2 signal, and initial nodal thickness on ultrasound. Breast cancer patients presenting with anterior tumours, no intratumoral high signal intensity on T2-weighted sequences, and initial nodal cortical thickening greater than 7 mm exhibited a higher risk of residual axillary disease post-NAC. In the training and test sets, the model, available online, achieved AUCs under ROC curves of 0.860 (95% CI: 0.783-0.936) and 0.843 (95% CI: 0.714-0.971), respectively.To identify reproducible MRI radiomic parameters, we developed a processing pipeline to harmonize radiomic indices across three MRI acquisition protocols using the ComBat method.Subsequently, we implemented a deep learning-based automated tumour segmentation method combining T1-weighted contrast-enhanced and subtraction images, to reduce radiologists' workload and improve robustness. We further evaluated the impact of additional MRI data acquired mid-treatment on the algorithm's performance. Using "no-new-Net" (nnU-Net) neural networks, we simplified pre-processing by eliminating the need for image cropping
Doyon, Caroline. "Thérapie prolongée au mesylate d'imatinib avant la chirurgie pour les tumeurs stromales gastrointestinales avancées : résultats d'une étude prospective de phase II." Thèse, 2010. http://hdl.handle.net/1866/6995.
Full textGastrointestinal stromal tumor (GIST) is the most common mesenchymal malignancy of the GI tract. The current standard of care for GIST is surgical complete resection with negative margins. The agent response rate as well as survival advantages obtained with imatinib mesylate in patients with metastatic and/or non-resectable GIST has lead clinicians to evaluate this therapy as neoadjuvant treatment in patients with locally advanced or metastatic but potentially resectable GIST. This study was designed to evaluate the efficacy of neoadjuvant use of imatinib mesylate until maximal clinical response in potentially resectable GIST patients (locally advanced or metastatic), in order to provide preliminary data regarding the efficacy of this approach in the surgical treatment of GIST at high-risk of incomplete microscopic (R1) or macroscopic (R2) margins. A prospective multicenter phase II trial was designed. Fourteen consecutive patients diagnosed with advanced GIST received imatinib at dose of 400 mg/d to 600 mg/d, given from 6 to 12 months prior to surgery. Amoung the 14 patients included, 11 underwent surgery and had a complete microscopic resection (R0). After a median follow-up of 48 months, all operated patients were alive and 7 without evidence of recurrence. The prolonged use (12 months) of neoadjuvant imatinib is feasible, safe, eficient ans associated with low toxicity. Furthermore, it is associated with a high rate of microscopic resection (R0) and a less extensive surgical approach. Phase III study with higher cohorts are necessary to confirm our primary results.