https://sms.carm.es/ricsmur/handle/123456789/17867 2026-04-15T16:40:34Z https://sms.carm.es/ricsmur/handle/123456789/25828 Prediction of oncogene mutation status in non-small cell lung cancer: a systematic review and meta-analysis with a special focus on artificial intelligence-based methods Fuster-Matanzo, Almudena; Picó-Peris, Alfonso; Bellvís-Bataller, Fuensanta; Jimenez-Pastor, Ana; Weiss, Glen J; Martí-Bonmatí, Luis; Lázaro-Sánchez, Antonio-David; Bazaga, David; Banna, Giuseppe L; Addeo, Alfredo; Camps, Carlos; Seijo, Luis M; Alberich-Bayarri, Ángel OBJECTIVES: In non-small cell lung cancer (NSCLC), non-invasive alternatives to biopsy-dependent driver mutation analysis are needed. We reviewed the effectiveness of radiomics alone or with clinical data and assessed the performance of artificial intelligence (AI) models in predicting oncogene mutation status. MATERIALS AND METHODS: A PRISMA-compliant literature review for studies predicting oncogene mutation status in NSCLC patients using radiomics was conducted by a multidisciplinary team. Meta-analyses evaluating the performance of AI-based models developed with CT-derived radiomics features alone or combined with clinical data were performed. A meta-regression to analyze the influence of different predictors was also conducted. RESULTS: Of 890 studies identified, 124 evaluating models for the prediction of epidermal growth factor-1 (EGFR), anaplastic lymphoma kinase (ALK), and Kirsten rat sarcoma virus (KRAS) mutations were included in the systematic review, of which 51 were meta-analyzed. The AI algorithms' sensitivity/false positive rate (FPR) in predicting mutation status using radiomics-based models was 0.754 (95% CI 0.727-0.780)/0.344 (95% CI 0.308-0.381) for EGFR, 0.754 (95% CI 0.638-0.841)/0.225 (95% CI 0.163-0.302) for ALK and 0.475 (95% CI 0.153-0.820)/0.181 (95% CI 0.054-0.461) for KRAS. A meta-analysis of combined models was possible for EGFR mutation, revealing a sensitivity of 0.806 (95% CI 0.777-0.833) and a FPR of 0.315 (95% CI 0.270-0.364). No statistically significant results were obtained in the meta-regression. CONCLUSIONS: Radiomics-based models may offer a non-invasive alternative for determining oncogene mutation status in NSCLC. Further research is required to analyze whether clinical data might boost their performance. KEY POINTS: Question Can imaging-based radiomics and artificial intelligence non-invasively predict oncogene mutation status to improve diagnosis in non-small cell lung cancer (NSCLC)? Findings Radiomics-based models achieved high performance in predicting mutation status in NSCLC; adding clinical data showed limited improvement in predictive performance. Clinical relevance Radiomics and AI tools offer a non-invasive strategy to support molecular profiling in NSCLC. Validation studies addressing clinical and methodological aspects are essential to ensure their reliability and integration into routine clinical practice. 2025-09-08T00:00:00Z https://sms.carm.es/ricsmur/handle/123456789/25821 Discordant Patch Test Reactions to 2-Bromo-2-Nitro-Propane-1,3-Diol (Bronopol): A Multicenter Study From REIDAC Sanz-Sánchez, Tatiana; Giménez-Arnau, Ana María; Zaragoza-Ninet, Violeta; Córdoba-Guijarro, Susana; Miquel-Miquel, Francisco Javier; Silvestre-Salvador, Juan-Francisco; González-Pérez, Ricardo; Ruiz-González, Inmaculada; Mercader-García, Pedro; Serra-Baldrich, Esther; Carrascosa-rrillo, José-Manuel; Tous-Romero, Fátima; Ortiz-de Frutos, Francisco-Javier; Rodríguez-Serna, Mercedes; Gatica-Ortega, María-Elena; Paredes-Suárez, Carmen; Navarro-Triviño, Francisco; Chicharro, Pablo; Pastor-Nieto, María-Antonia; Gómez de la Fuente, Enrique; Sánchez-Gilo, Araceli; Andreu-Barasoain, Marta; Pereyra Rodríguez, José Juan; Melé-i-Ninot, Gemma; Sánchez-Pedreño-Guillén, Paloma; Elosua-González, Marta; Grau-Pérez, Mercè; Descalzo, Miguel Ángel; Borrego, Leopoldo 2026-04-01T00:00:00Z https://sms.carm.es/ricsmur/handle/123456789/25819 Pilot clinical comparison of three occlusal splint fabrication techniques: A preliminary study Torné-Durán, Sergi; Marco-Martínez, Laura; Serrano-Belmonte, Ildefonso To compare laboratory production time, clinical adjustment time, and patient-reported comfort of three occlusal splint fabrication techniques (heat-cured acrylic, vacuum-adapted acrylic, and CAD-CAM 3D-printed splints) in a pilot feasibility study. Three participants each received three splints, one fabricated with each technique. Laboratory production time, chairside adjustment time, and comfort (VAS) were recorded. Vacuum-adapted splints required the shortest laboratory production time (mean = 92 min, SD = 25.35). Heat-cured splints required longer processing (mean = 114 min, SD = 6.08). The CAD-CAM splints showed the longest total workflow duration (mean = 133 min, SD = 6.08), although they required less manual technician work. Intraoral adjustment times were similar between heat-cured and vacuum-adapted splints (means = 28 min and 26.66 min, respectively). None of the CAD-CAM splints seated fully at delivery, preventing proper adjustment. Vacuum-adapted splints received the highest comfort scores. Within the limitations of this pilot study with three participants, vacuum-adapted and heat-cured splints showed clinically acceptable performance and comparable adjustment times. CAD-CAM splints reduced manual workload but suffered from significant seating and fit issues, indicating the need for workflow refinement before clinical implementation. Even as splint fabrication is moving towards a more digital workflow, the old methods, especially vacuum-adapted splints, continue to deliver timely and comfortable results to patients. Further studies with more participants need to be done so that there can be a clear digital splint fabrication workflow. 2025-12-19T00:00:00Z https://sms.carm.es/ricsmur/handle/123456789/25777 A critical analysis of the IMWG multiple myeloma complete response criterion in the era of mass spectrometry Puig, Noemi; Agullo, Cristina; Paiva, Bruno; Cedeña, María-Teresa; Rosinol, Laura; Contreras, Teresa; Martínez-lopez, Joaquín; Oriol, Albert; Blanchard, María-Jesús; Ríos-tamayo, Rafael; Sureda, Anna; Lakhwani, Sunil; de-La-Rubia, Javier; Cabanas-Perianes, Valentín; de-Arriba-de-la-Fuente, Felipe; Paricio, Miguel; Inigo, María-Belén; González-calle, Verónica; Ocio, Enrique-M; Castro, Sergio; Bargay, Joan; Blade, Joan; San-Miguel, Jesus-F; Lahuerta, Juan-José; Mateos, María-Victoria 2026-02-01T00:00:00Z