Anna Scavuzzo

Non-muscle-invasive bladder cancer (NMIBC) accounts for most newly diagnosed bladder tumors, yet little is known about how it is managed in Mexico, particularly during prolonged Bacillus Calmette-Guérin (BCG) shortages. This retrospective, multicenter observational study characterized the clinical and histopathological profile, the diagnostic and treatment timelines, and the adherence to guideline-recommended care of patients with NMIBC treated at three tertiary referral hospitals in Mexico City. The records of 221 adults diagnosed between January 2010 and December 2020 were reviewed, and the risk of recurrence or progression was classified according to the European Association of Urology 2019 classification. The median age was 68 years (interquartile range [IQR] 59–75) and the male-to-female ratio was 3:1. Hematuria was the most frequent presenting symptom (83.3%). Three-quarters of patients had T1 tumors, 6.3% had concomitant carcinoma in situ, 84.6% had high-grade tumors, and 92.8% were at high risk of recurrence or progression. The median interval from symptom onset to histopathologic diagnosis was 167 days (IQR 81–329) and varied between centers. After diagnosis, the median time to second-look resection was 67 days (IQR 33.5–112) and to the first BCG instillation 92.5 days (IQR 41.8–165.5). Adjuvant intravesical therapy was administered to 66% of patients, most often BCG, although fewer than half of the BCG-treated patients (45%) completed induction, and 40% of the patients eligible for a second-look transurethral resection did not undergo it. Thirty percent of patients had no documented post-treatment surveillance. These real-world data provide a baseline description of NMIBC care in Mexican tertiary centers and identify specific, measurable gaps that may inform standardization and quality-improvement efforts in resource-constrained settings.

Testicular germ cell tumors (TGCTs) are the most common solid malignancies in young men and, despite high cure rates with cisplatin-based multimodal therapy, a clinically relevant subset of patients develops relapsed, platinum-refractory, or treatment-resistant disease with limited salvage options and substantial long-term morbidity. This unmet need has renewed interest in immunotherapy, yet the experience in TGCT has been notably less successful than in other malignancies. Although TGCTs often display PD-L1 expression, tumor-infiltrating lymphocytes, and other features suggestive of immune engagement, immune checkpoint inhibitors have produced largely disappointing results in unselected patients, underscoring the complexity of immune regulation in this immune-privileged disease. Recent conceptual advances place microRNAs (miRNAs) at the center of this problem. Beyond their established diagnostic and prognostic value, miRNAs are increasingly recognized as upstream regulators of immune checkpoints, antigen-presentation pathways, cytokine signaling, macrophage polarization, dendritic-cell and T-cell function, and extracellular-vesicle-mediated tumor–immune crosstalk. This positions miRNAs not only as biomarkers of disease activity, but also as plausible determinants of immunotherapy sensitivity or resistance. At the same time, the field remains marked by important controversies: PD-L1 expression alone is an unreliable predictor of response; the relative contribution of low tumor mutational burden, testicular immune privilege, and microenvironmental suppression remains unresolved; and many proposed TGCT-associated miRNAs with an immunoregulatory role are still inferential rather than causally validated in disease-specific models. Accordingly, major gaps persist in defining which miRNAs are true functional drivers of immune escape in seminomatous versus non-seminomatous TGCT, how they interact with therapy-induced tumor evolution, and which delivery platforms can achieve safe, tumor-directed modulation. We argue that the next phase of the field should move beyond descriptive biomarker studies toward mechanism-based, TGCT-specific translational strategies integrating miRNA mimics, antagomirs, extracellular vesicles, or miRNA-augmented cellular therapies with immune-priming approaches such as epigenetic therapy, radiotherapy, vaccines, or CAR-T platforms. Such a framework could enable more precise biological stratification and improve the efficacy, durability, and tolerability of immunotherapy in refractory TGCT.

Genomic characterization of metastatic prostate cancer (mPCa) plays a pivotal role in guiding precision oncology. This study aimed to evaluate the feasibility of combining radiomics and clinical data within a machine learning (ML) framework to non-invasively predict key genomic mutations in patients with mPCa undergoing PSMA PET imaging. A retrospective cohort of 14 mPCa patients who underwent [¹⁸ F]PSMA-1007 PET/CT was analysed. Prostate and metastatic lesions were segmented, and radiomics features were extracted. Somatic genomic alterations were obtained from formalin-fixed paraffin-embedded tissue samples using FoundationOne CDx testing. Six ML algorithms - Discriminant Analysis, Support Vector Machines, K-Nearest Neighbours, Neural Networks, Random Forest, and Boosting - were trained using a 5-times repeated pipeline with 80/20 train/test split, LASSO feature selection, and 5-fold cross-validation. Model performance was assessed using accuracy, AUC, sensitivity, specificity, precision, and F-score. Fourteen patients with mPCa were included, and 46 lesions were analysed. Genomic alterations included mutations in TP53, TMPRSS2, PTEN, BRCA1/2, ATM, and others. Owing to data limitations, mutations other than TP53, TMPRSS2, and PTEN were grouped into a composite "OTHER" category. The best-performing clinical-radiomics ML models achieved AUCs of 91.11% (TP53), 84.44% (TMPRSS2), 80.00% (PTEN), and 77.78% (OTHER). Selected feature stability was consistent across repeated runs. Clinical-radiomics ML models based on PSMA PET imaging show promising accuracy in predicting actionable genomic alterations in mPCa. These findings support further investigation into radiogenomics modelling as a complementary, non-invasive tool to inform molecular profiling and treatment stratification.

Introduction: Epidemiological data are crucial for adopting primary and secondary prevention strategies and to develop screening protocols against prostate cancer (PCa). Despite the comprehensive characterization of PCa across White and Black men, there is a lack of data from the Mexican population. This manuscript presents data from the Can.Prost registry that captures PCa trends over the past two decades in Mexico City; furthermore, we aimed to compare clinical differences and oncological outcomes before and after the promotion of early detection actions through a campaign against PCa that occurred in 2014. Methods: A retrospective observational study on newly diagnosed Mexican PCa patients was carried out at the Instituto Nacional de Cancerología (INCan) in Mexico City. During 2014 and 2015, a project for the early diagnosis of PCa (“OPUS program”) was launched in the aforementioned tertiary hospital. Starting at the age of 45 years, all men were invited for a PSA measurement and a specialist urologist consultation. All individuals with clinical or biochemical suspicion of PCa (PSA > 4 ng/mL), in the context of age and prostate volume, underwent ultrasound-guided transrectal prostate biopsy. Then, patients with pathologically confirmed prostate cancer were stratified according to the year of diagnosis: Group A accounted for those diagnosed between 2000 and 2014 and Group B for those patients diagnosed in the timeframe of 2015–2021. Comparisons of PCa characteristics, treatment modalities and oncologic outcomes between Group A and B were performed. Results: Overall, we collected data from 2759 PCa patients from 2000 to 2021. The median PSA at baseline was 32 ng/mL, and 25% had a family history of PCa. Overall, 25.8% were asymptomatic and 46% had a non-metastatic presentation. After the OPUS campaign, PSA at diagnosis was significantly lower across all age groups. The incidence of PCa diagnosis in asymptomatic men was higher (31.4% vs. 19.9%) and a higher proportion of men were diagnosed with organ-confined, palpable disease (46% vs. 28%) (p < 0.001). The rate of patients eligible for active/radical treatment was higher after the OPUS campaign (patients who received surgery increased from 12.78% to 32.41%; patients who underwent radiation increased from 28.38% to 49.61%). The proportion of patients diagnosed with non-clinically significant disease was negligible and remained stable across time. Conclusions: PCa in Mexican patients displays aggressive features at diagnosis, whereas the rate of non-significant disease is negligible. The introduction of early detection strategies may lead to lower symptomatic and metastatic PCa and higher opportunities for radical treatment. This emphasizes the need for public awareness and for adjustment of screening strategies to the peculiarities of the Mexican population.

Background: Health disparities have been highlighted among patient with prostate adenocarcinoma (PRAD) due to ethnicity. Mexican men present a more aggressive disease than other patients resulting in less favorable treatment outcome. We aimed to identify the mutational landscape which could help to reduce the health disparities among minority groups and generate the first genomics exploratory study of PRAD in Mexican patients. Methods: Paraffin-embedded formalin-fixed tumoral tissue from 20 Mexican patients with early-stage PRAD treated at The Instituto Nacional de Cancerología, Mexico City from 2017 to 2019 were analyzed. Tumoral DNA was prepared for whole exome sequencing, the resulting files were mapped against h19 using BWA-MEM. Strelka2 and Lancet packages were used to identify single nucleotide variants (SNV) and insertions or deletions. FACETS was used to determine somatic copy number alterations (SCNA). Cancer Genome Interpreter web interface was used to determine the clinical relevance of variants. Results: Patients were in an early clinical stage and had a mean age of 59.55 years (standard deviation [SD]: 7.1 years) with 90% of them having a Gleason Score of 7. Follow-up time was 48.50 months (SD: 32.77) with recurrences and progression in 30% and 15% of the patients, respectively. NUP98 (20%), CSMD3 (15%) and FAT1 (15%) were the genes most frequently affected by SNV; ARAF (75%) and ZNF419 (70%) were the most frequently affected by losses and gains SNCA’s. One quarter of the patients had mutations useful as biomarkers for the use of PARP inhibitors, they comprise mutations in BRCA, RAD54L and ATM. SBS05, DBS03 and ID08 were the most common mutational signatures present in this cohort. No associations with recurrence or progression were identified. Conclusions: This pilot study reveals the mutational landscape of early-stage prostate adenocarcinoma in Mexican men, providing a first approach to understand the mutational patterns and actionable mutations in early prostate cancer can inform personalized treatment approaches and reduce the underrepresentation in genomic cancer studies.

Prostate cancer is a leading cause of cancer death in men worldwide. Imaging plays a key role in disease detection and initial staging. Emerging data has shown the superiority of PSMA imaging with PET/CT over conventional imaging for primary diagnoses. Single photon emission computed tomography is more available worldwide, and the imaging agent is low in cost. The aim of this study is to compare the diagnostic accuracy of ^99mTc-EDDA/HYNIC-iPSMA SPECT/CT to ¹⁸F-PSMA-1007 PET/CT in the primary diagnosis of prostate cancer and the impact on clinical staging. Methods: In this prospective controlled study, 18 patients with histologically confirmed prostate cancer with unfavorable intermediate-, high-, and very high-risk characteristics were recruited to undergo ¹⁸F-PSMA-PET/CT and ^99mTc-iPSMA SPECT/CT. The median age of the patients was 71 years old, and the median PSA level was 23.3 ng/mL. Lesions were divided into the prostate, seminal vesicles, lymph nodes, bone, and visceral metastases. Volumetric analysis was also performed between the two imaging modalities and correlated with PSA levels. Results: A total of 257 lesions were detected on ¹⁸F-PSMA-PET/CT: prostate (n = 18), seminal vesicles (n = 12), locoregional lymph nodes (n = 62), non-locoregional (n = 67), bone (n = 90), and visceral (n = 8). Of these, ^99mTc-iPSMA-SPECT/CT detected 229 lesions, while both reviewers detected 100% of the lesions in the prostate (18/18), seminal vesicles (12/12), and visceral (8/8); LN LR (56/62; 90%), NLR (57/67; 85%), and bone (78/90; 86%). There were no statistically significant differences between volumetric parameters (t = −0.02122; p = 0.491596). Conclusions: ^99mTc-iPSMA SPECT/CT is useful in the primary diagnosis of prostate cancer. Despite it showing a slightly lower lesion detection rate compared to ¹⁸F-PSMA PET/CT, it exhibited no impact on clinical staging and, consequently, the initial treatment intention.

Background: The identification of histopathology in metastatic non-seminomatous testicular germ cell tumors (TGCT) before post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) holds significant potential to reduce treatment-related morbidity in young patients, addressing an important survivorship concern. Aim: To explore this possibility, we conducted a study investigating the role of computed tomography (CT) radiomics models that integrate clinical predictors, enabling personalized prediction of histopathology in metastatic non-seminomatous TGCT patients prior to PC-RPLND. In this retrospective study, we included a cohort of 122 patients. Methods: Using dedicated radiomics software, we segmented the targets and extracted quantitative features from the CT images. Subsequently, we employed feature selection techniques and developed radiomics-based machine learning models to predict histological subtypes. To ensure the robustness of our procedure, we implemented a 5-fold cross-validation approach. When evaluating the models’ performance, we measured metrics such as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, precision, and F-score. Result: Our radiomics model based on the Support Vector Machine achieved an optimal average AUC of 0.945. Conclusions: The presented CT-based radiomics model can potentially serve as a non-invasive tool to predict histopathological outcomes, differentiating among fibrosis/necrosis, teratoma, and viable tumor in metastatic non-seminomatous TGCT before PC-RPLND. It has the potential to be considered a promising tool to mitigate the risk of over- or under-treatment in young patients, although multi-center validation is critical to confirm the clinical utility of the proposed radiomics workflow.

Antecedentes. La función sexual masculina es una interacción compleja de diversos factores psicológicos, neurogénicos y vasculares. Una interrupción en cualquier parte de este proceso conduce a una disfunción sexual. En México, la disfunción sexual afecta a entre 30 y 35% de los varones, por lo que es necesario contar con un instrumento válido y confiable que mida la funcionalidad sexual en esta población. Objetivo. Determinar las propiedades psicométricas del Índice Compuesto para el Cáncer de Próstata (EPIC) para la valoración sexual en hombres mexicanos. Método. Estudio transversal, instrumental y no experimental en el que participaron 206 hombres. Instrumentos. Índice Compuesto Expandido para Función Sexual y la Evaluación Funcional de Enfermedades Crónicas en Población General. Resultados. Se obtuvo para el instrumento una consistencia interna de .93 y una varianza explicada de 67.6%. El análisis factorial confirmatorio arrojó un modelo de dos factores (Funcionalidad sexual y Malestar sexual), con índices que sugieren estabilidad y ajuste. Discusión. La versión mexicana del EPIC tiene confiabilidad y validez adecuadas, siendo su estructura factorial similar a la de la escala original, por lo que se recomienda para la práctica clínica y la investigación, a la vez que se sugiere emplearlo en varones con diversas afectaciones sexuales.

Antecedentes: El cáncer es un problema de salud pública, una de las variables psicosociales en relación con esta enfermedad es el estrés, por tanto es necesario tener una escala que mida el estrés en población hispana con cáncer. Objetivo: Determinar las propiedades psicométricas de la Escala de Medida del Estado Actual (MOCS, por sus igla en inglés) en pacientes mexicanos con cáncer. Método: Estudio transversal, instrumental no experimental. Participaron 197 pacientes de 18 a 81 años, de cualquier etapa clínica, en tratamiento o seguimiento del cáncer. Instrumentos: MOCS y termómetro de malestar emocional. Análisis estadísticos: análisis factorial confirmatorio para evaluar el ajuste del modelo a los datos mediante el método de máxima verosimilitud y puntos de corte. Resultados: Se obtuvieron dos modelos, el primero, estado actual autopercibido, con un alfa de .90 varianza explicada de 74.2 %, el segundo, efectos inespecíficos, con un alfa de .61 varianza explicada de 74.3 %. El análisis factorial confirmatorio mostró que ambos modelos tuvieron índices que sugieren estabilidad, ajuste aceptable, se muestran puntajes mediante rangos intercuartílicos para cada modelo. Conclusiones: la versión mexicana del MOCS tiene confiabilidad y validez, su estructura factorial confirmatoria es similar a la versión original. Se recomienda su uso en la práctica clínica e investigación en pacientes con cáncer.

Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumor (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomic analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016–2021. A prospective group (A) of 30 patients undergoing CT was segmented using the 3D Slicer software while a retrospective group (B) of 30 patients was evaluated with conventional CT (without 3D reconstruction). CatFisher’s exact test showed a p-value of 0.13 for group A and 1.0 for Group B. The difference between the proportion test showed a p-value of 0.009149 (IC 0.1–0.63). The proportion of the correct classification showed a p-value of 0.645 (IC 0.55–0.87) for A, and 0.275 (IC 0.11–0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, and surface area, among others. Performing a logistic regression with the entire dataset, n = 60, the results were: Accuracy: 0.7 and Precision: 0.65. Using n = 30 randomly chosen, the best result obtained was Accuracy: 0.73 and Precision: 0.83, with a p-value: 0.025 for Fisher’s exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeons versus experienced surgeons. Radiomic features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing it to plan the surgery and to anticipate complications.