arXiv:2511.17158v1 Announce Type: cross 
Abstract: Objectives: To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Materials \& Methods: Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. Results: Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n=7/37, 19%), triple negative (TN) (n=30/55, 55%), HER2+ (n=22/37, 59%). Clinical and biological items associated with pCR were BC subtype (p<0.001), T stage 0/I/II (p=0.008), higher Ki67 (p=0.005) and higher tumor-infiltrating lymphocytes levels (p=0.016). Univariate analysis showed that the following MRI features, oval or round shape (p=0.047), unifocality (p=0.026), non-spiculated margins (p=0.018), no associated non-mass enhancement (NME) (p = 0.024) and a lower MRI size (p = 0.031) were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67) and precision (0.71 versus 0.67) for pCR prediction. Conclusion: Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. Clinical Relevance Statement: A multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including TILs, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes

أجرت دراسة حديثة تقييمًا للعلاقة بين أوصاف التصوير بالرنين المغناطيسي قبل العلاج والاستجابة المرضية الكاملة (pCR) لدى مرضى سرطان الثدي الذين يخضعون للعلاج الكيميائي المساعد. شملت التحليل الرجعي 129 مريضًا تم علاجهم بين عامي 2016 و2020، وكشفت أن 46% منهم حققوا pCR، مع ملاحظات لوجود اختلافات ملحوظة بين أنواع سرطان الثدي.

Un estudio reciente evaluó la asociación entre los descriptores de resonancia magnética (RM) previos al tratamiento y la respuesta patológica completa (pCR) en pacientes con cáncer de mama que recibieron quimioterapia neoadyuvante. El análisis retrospectivo incluyó a 129 pacientes tratados entre 2016 y 2020, revelando que el 46% alcanzó pCR, con diferencias significativas observadas entre los subtipos de cáncer de mama.

Une étude récente a évalué l'association entre les descripteurs IRM prétraitement et la réponse pathologique complète (pCR) des patientes atteintes de cancer du sein subissant une chimiothérapie néoadjuvante. L'analyse rétrospective a inclus 129 patientes traitées entre 2016 et 2020, révélant que 46 % avaient atteint la pCR, avec des différences significatives notées selon les sous-types de cancer du sein.

A recent study evaluated the association between pretreatment MRI descriptors and the pathologic complete response (pCR) of breast cancer patients undergoing neoadjuvant chemotherapy. The retrospective analysis included 129 patients treated between 2016 and 2020, revealing that 46% achieved pCR, with significant differences noted across breast cancer subtypes.

Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy

arXiv:2511.17068v1 Announce Type: new 
Abstract: Magnetic Resonance Imaging (MRI) plays a crucial role in brain disease diagnosis, but it is not always feasible for certain patients due to physical or clinical constraints. Recent studies attempt to synthesize MRI from Computed Tomography (CT) scans; however, low-dose protocols often result in highly sparse CT volumes with poor through-plane resolution, making accurate reconstruction of the full brain MRI volume particularly challenging. To address this, we propose ReBrain, a retrieval-augmented diffusion framework for brain MRI reconstruction. Given any 3D CT scan with limited slices, we first employ a Brownian Bridge Diffusion Model (BBDM) to synthesize MRI slices along the 2D dimension. Simultaneously, we retrieve structurally and pathologically similar CT slices from a comprehensive prior database via a fine-tuned retrieval model. These retrieved slices are used as references, incorporated through a ControlNet branch to guide the generation of intermediate MRI slices and ensure structural continuity. We further account for rare retrieval failures when the database lacks suitable references and apply spherical linear interpolation to provide supplementary guidance. Extensive experiments on SynthRAD2023 and BraTS demonstrate that ReBrain achieves state-of-the-art performance in cross-modal reconstruction under sparse conditions.

تم تقديم إطار جديد يسمى ReBrain لإعادة بناء التصوير بالرنين المغناطيسي للدماغ من شرائح التصوير المقطعي المحوسب النادرة باستخدام نهج تعزيز الاسترجاع عبر الانتشار. تستخدم هذه الطريقة نموذج انتشار جسر براوني لتوليد شرائح التصوير بالرنين المغناطيسي واسترجاع شرائح التصوير المقطعي المحوسب المماثلة من قاعدة بيانات سابقة لتحسين دقة إعادة البناء.

Se ha introducido un nuevo marco llamado ReBrain para reconstruir la IRM cerebral a partir de cortes de TC escasos utilizando un enfoque de difusión aumentada por recuperación. Este método emplea un Modelo de Difusión de Puente Browniano para sintetizar cortes de IRM y recupera cortes de TC similares de una base de datos previa para mejorar la precisión de la reconstrucción.

Un nouveau cadre nommé ReBrain a été introduit pour reconstruire l'IRM cérébrale à partir de coupes CT rares en utilisant une approche de diffusion augmentée par récupération. Cette méthode utilise un modèle de diffusion de pont brownien pour synthétiser des coupes IRM et récupère des coupes CT similaires à partir d'une base de données antérieure pour améliorer la précision de la reconstruction.

A new framework named ReBrain has been introduced for reconstructing brain MRI from sparse CT slices using a retrieval-augmented diffusion approach. This method utilizes a Brownian Bridge Diffusion Model to synthesize MRI slices and retrieves similar CT slices from a prior database to enhance reconstruction accuracy.

ReBrain: Brain MRI Reconstruction from Sparse CT Slice via Retrieval-Augmented Diffusion

arXiv:2511.17485v1 Announce Type: new 
Abstract: The human spine is a complex structure composed of 33 vertebrae. It holds the body and is important for leading a healthy life. The spine is vulnerable to age-related degenerations that can be identified through magnetic resonance imaging (MRI). In this paper we propose a novel computer-vison-based deep learning method to estimate spine age using images from over 18,000 MRI series. Data are restricted to subjects with only age-related spine degeneration. Eligibility criteria are created by identifying common age-based clusters of degenerative spine conditions using uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN). Model selection is determined using a detailed ablation study on data size, loss, and the effect of different spine regions. We evaluate the clinical utility of our model by calculating the difference between actual spine age and model-predicted age, the spine age gap (SAG), and examining the association between these differences and spine degenerative conditions and lifestyle factors. We find that SAG is associated with conditions including disc bulges, disc osteophytes, spinal stenosis, and fractures, as well as lifestyle factors like smoking and physically demanding work, and thus may be a useful biomarker for measuring overall spine health.

تم تطوير إطار جديد للذكاء الاصطناعي لقياس شيخوخة العمود الفقري البشري باستخدام التصوير بالرنين المغناطيسي، مستفيدًا من طريقة تعلم عميقة تحلل أكثر من 18,000 سلسلة من صور الرنين المغناطيسي التي تركز على تدهور العمود الفقري المرتبط بالعمر. يستخدم النموذج تقنيات تجميع متقدمة لتحديد الحالات التدهورية ويقيم فائدته السريرية من خلال مقارنة العمر الفعلي للعمود الفقري مع توقعات النموذج.

Se ha desarrollado un nuevo marco de inteligencia artificial para medir el envejecimiento de la columna vertebral humana utilizando resonancias magnéticas (IRM), aprovechando un método de aprendizaje profundo que analiza más de 18,000 series de IRM centradas en la degeneración espinal relacionada con la edad. El modelo emplea técnicas avanzadas de agrupamiento para identificar condiciones degenerativas y evalúa su utilidad clínica comparando la edad real de la columna con las predicciones del modelo.

Un nouveau cadre d'intelligence artificielle a été développé pour mesurer le vieillissement de la colonne vertébrale humaine à l'aide d'IRM, en s'appuyant sur une méthode d'apprentissage profond qui analyse plus de 18 000 séries d'IRM axées sur la dégénérescence vertébrale liée à l'âge. Le modèle utilise des techniques de clustering avancées pour identifier les conditions dégénératives et évalue son utilité clinique en comparant l'âge réel de la colonne vertébrale aux prédictions du modèle.

A novel artificial intelligence framework has been developed to measure human spine aging using MRI, leveraging a deep learning method that analyzes over 18,000 MRI series focused on age-related spine degeneration. The model employs advanced clustering techniques to identify degenerative conditions and evaluates its clinical utility by comparing actual spine age with model predictions.

Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy

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