arXiv:2510.21140v1 Announce Type: new 
Abstract: Computed Tomography Pulmonary Angiography (CTPA) is the reference standard for diagnosing pulmonary vascular diseases such as Pulmonary Embolism (PE) and Chronic Thromboembolic Pulmonary Hypertension (CTEPH). However, its reliance on iodinated contrast agents poses risks including nephrotoxicity and allergic reactions, particularly in high-risk patients. This study proposes a method to generate Digital Contrast CTPA (DCCTPA) from Non-Contrast CT (NCCT) scans using a cascaded synthesizer based on Cycle-Consistent Generative Adversarial Networks (CycleGAN). Totally retrospective 410 paired CTPA and NCCT scans were obtained from three centers. The model was trained and validated internally on 249 paired images. Extra dataset that comprising 161 paired images was as test set for model generalization evaluation and downstream clinical tasks validation. Compared with state-of-the-art (SOTA) methods, the proposed method achieved the best comprehensive performance by evaluating quantitative metrics (For validation, MAE: 156.28, PSNR: 20.71 and SSIM: 0.98; For test, MAE: 165.12, PSNR: 20.27 and SSIM: 0.98) and qualitative visualization, demonstrating valid vessel enhancement, superior image fidelity and structural preservation. The approach was further applied to downstream tasks of pulmonary vessel segmentation and vascular quantification. On the test set, the average Dice, clDice, and clRecall of artery and vein pulmonary segmentation was 0.70, 0.71, 0.73 and 0.70, 0.72, 0.75 respectively, all markedly improved compared with NCCT inputs.\@ Inter-class Correlation Coefficient (ICC) for vessel volume between DCCTPA and CTPA was significantly better than that between NCCT and CTPA (Average ICC : 0.81 vs 0.70), indicating effective vascular enhancement in DCCTPA, especially for small vessels.

تقدم دراسة جديدة طريقة مبتكرة لإنشاء تصوير الأوعية الرئوية باستخدام الأشعة المقطعية الرقمية من الأشعة المقطعية بدون تباين، مما يعالج المخاطر المرتبطة بعوامل التباين اليودية التقليدية. هذه الابتكار مهم بشكل خاص للمرضى ذوي المخاطر العالية الذين قد يعانون من سمية الكلى أو ردود فعل تحسسية. من خلال تحسين قدرات التشخيص مع تقليل الأضرار المحتملة، يمكن أن تحول هذه الخطوة الطريقة التي يتم بها تشخيص وإدارة الأمراض الوعائية الرئوية مثل الانسداد الرئوي والارتفاع المزمن لضغط الدم الرئوي الناتج عن الجلطات.

Un nuevo estudio presenta un método innovador para generar angiografía pulmonar por tomografía computarizada digital a partir de tomografías sin contraste, abordando los riesgos asociados con los agentes de contraste yodados tradicionales. Esta innovación es especialmente importante para los pacientes de alto riesgo que pueden sufrir nefrotoxicidad o reacciones alérgicas. Al mejorar las capacidades de diagnóstico mientras se minimizan los posibles daños, este avance podría transformar la forma en que se diagnostican y gestionan enfermedades vasculares pulmonares como la embolia pulmonar y la hipertensión pulmonar tromboembólica crónica.

Une nouvelle étude propose une méthode révolutionnaire pour créer une angiographie pulmonaire par tomodensitométrie numérique à partir de scans sans contraste, répondant ainsi aux risques associés aux agents de contraste iodés traditionnels. Cette innovation est particulièrement significative pour les patients à haut risque qui peuvent souffrir de néphrotoxicité ou de réactions allergiques. En améliorant les capacités de diagnostic tout en minimisant les risques potentiels, cette avancée pourrait transformer la manière dont les maladies vasculaires pulmonaires telles que l'embolie pulmonaire et l'hypertension pulmonaire thromboembolique chronique sont diagnostiquées et gérées.

A new study introduces a groundbreaking method to create Digital Contrast CT Pulmonary Angiography from Non-Contrast CT scans, addressing the risks associated with traditional iodinated contrast agents. This innovation is particularly significant for high-risk patients who may suffer from nephrotoxicity or allergic reactions. By enhancing diagnostic capabilities while minimizing potential harm, this advancement could transform the way pulmonary vascular diseases like Pulmonary Embolism and Chronic Thromboembolic Pulmonary Hypertension are diagnosed and managed.

Digital Contrast CT Pulmonary Angiography Synthesis from Non-contrast CT for Pulmonary Vascular Disease

arXiv:2601.08455v1 Announce Type: new 
Abstract: Objectives: High-grade serous ovarian carcinoma (HGSOC) is typically diagnosed at an advanced stage with extensive peritoneal metastases, making treatment challenging. Neoadjuvant chemotherapy (NACT) is often used to reduce tumor burden before surgery, but about 40% of patients show limited response. Radiomics, combined with machine learning (ML), offers a promising non-invasive method for predicting NACT response by analyzing computed tomography (CT) imaging data. This study aimed to improve response prediction in HGSOC patients undergoing NACT by integration different feature selection methods. Materials and methods: A framework for selecting robust radiomics features was introduced by employing an automated randomisation algorithm to mimic inter-observer variability, ensuring a balance between feature robustness and prediction accuracy. Four response metrics were used: chemotherapy response score (CRS), RECIST, volume reduction (VolR), and diameter reduction (DiaR). Lesions in different anatomical sites were studied. Pre- and post-NACT CT scans were used for feature extraction and model training on one cohort, and an independent cohort was used for external testing. Results: The best prediction performance was achieved using all lesions combined for VolR prediction, with an AUC of 0.83. Omental lesions provided the best results for CRS prediction (AUC 0.77), while pelvic lesions performed best for DiaR (AUC 0.76). Conclusion: The integration of robustness into the feature selection processes ensures the development of reliable models and thus facilitates the implementation of the radiomics models in clinical applications for HGSOC patients. Future work should explore further applications of radiomics in ovarian cancer, particularly in real-time clinical settings.

أظهرت دراسة حديثة تطوير نماذج راديو ميكولوجيا تنبؤية وموثوقة تهدف إلى تقييم استجابة العلاج الكيميائي لدى المرضى المصابين بسرطان المبيض الحاد عالي الدرجة (HGSOC)، وهو سرطان يتم تشخيصه عادة في مراحل متقدمة. تستخدم الدراسة تقنيات التعلم الآلي لتحليل بيانات التصوير المقطعي المحوسب، مما يعزز من دقة التنبؤ باستجابة العلاج الكيميائي المساعد.

Un estudio reciente ha desarrollado modelos de radiómica predictivos y robustos destinados a evaluar la respuesta a la quimioterapia en pacientes con carcinoma seroso de ovario de alto grado (HGSOC), un cáncer que generalmente se diagnostica en una etapa avanzada. La investigación utiliza técnicas de aprendizaje automático para analizar datos de imágenes por tomografía computarizada, mejorando así la predicción de la respuesta a la quimioterapia neoadyuvante.

Une étude récente a développé des modèles de radiomique prédictifs et robustes visant à évaluer la réponse à la chimiothérapie chez les patientes atteintes de carcinome ovarien séreux de haut grade (HGSOC), un cancer généralement diagnostiqué à un stade avancé. La recherche utilise des techniques d'apprentissage automatique pour analyser les données d'imagerie par tomodensitométrie, améliorant ainsi la prédiction de la réponse à la chimiothérapie néoadjuvante.

A recent study has developed predictive and robust radiomics models aimed at assessing chemotherapy response in patients with high-grade serous ovarian carcinoma (HGSOC), a cancer typically diagnosed at an advanced stage. The research utilizes machine learning techniques to analyze computed tomography imaging data, enhancing the prediction of neoadjuvant chemotherapy response.

Digital Contrast CT Pulmonary Angiography Synthesis from Non-contrast CT for Pulmonary Vascular Disease

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