Daniela Cristina Stefan

Background/Objectives: Pelvic exenteration is among the most aggressive procedures in surgical oncology and remains associated with substantial morbidity and highly variable oncologic outcomes. Achieving negative resection margins (R0) is the strongest determinant of long-term survival, yet accurate preoperative prediction of resectability remains difficult. This study evaluated oncologic outcomes following pelvic exenteration and explored the feasibility of a predictive modeling framework for estimating the probability of achieving R0 resection using routinely available pretherapeutic variables. Methods: A retrospective single-center cohort study included 229 patients undergoing pelvic exenteration for advanced pelvic disease between 2008 and 2024. Survival outcomes were analyzed using Kaplan–Meier and Cox regression methods. An exploratory machine-learning framework based on Random Forest (RF) and Extreme Gradient Boosting (XGB) algorithms was developed using demographic, oncologic, radiologic, therapeutic, and biological preoperative variables to estimate the likelihood of achieving negative surgical margins. Results: Median overall survival (OS) was 40 months (95%CI: 37–42 months), while median progression-free survival (PFS) was 18 months (95%CI: 17–19 months). Recurrent disease was associated with significantly worse oncologic outcomes, whereas colorectal malignancies demonstrated superior survival compared with urologic and gynecologic tumors. Multivariable analysis identified resection-margin status as the only independent predictor significantly associated with both OS (HR 1.338, 95%CI 1.211–1.764, p = 0.0137) and PFS (HR 3.499, 95%CI 2.357–5.195, p < 0.0001). RF achieved higher apparent overall accuracy (83.6%) but failed to identify positive-margin cases. XGB demonstrated more balanced discriminatory performance, with an ROC-AUC of 0.792 and improved identification of incomplete resections. Absence of radiologically visible loco-regional lymphadenopathy emerged as the strongest predictor of achieving R0 resection. Conclusions: Pelvic exenteration continues to provide meaningful oncologic benefit in selected patients when R0 resection is achievable. Although the predictive models remain exploratory and are not suitable for direct clinical implementation, they demonstrate the feasibility of integrating multidimensional pretherapeutic variables into decision-support frameworks for patient selection.

Following the post-publication review process, the authors wish to provide an additional clarification regarding the methodological approach used in this study

Background/Objectives: In 2020, the World Health Organization (WHO) launched a strategy to eliminate cervical cancer by ensuring high vaccination coverage, effective screening, and access to treatment. This review assesses how this strategy has been implemented in different world regions, what results have been achieved, and what disparities and challenges have been faced. Methods: A systematic search was performed in PubMed, Web of Science, GLOBOCAN, IARC, ASCO, and ESMO for publications issued between November 2020 and December 2024. A total of 721 records were reviewed and assessed, resulting in the inclusion of 47 studies in the final analysis. Results: Considerable regional variations were identified between the regions analyzed. Countries with organized vaccination programs, structured screening systems, and stronger treatment infrastructure generally reported more favorable outcomes. In contrast, several settings in Eastern Europe, parts of Asia, and sub-Saharan Africa remained below the WHO targets, with lower vaccination uptake, limited screening participation, and a persistently high cervical cancer burden. Progress was further constrained by inequalities in access to healthcare, differences in program organization, and limited comparability of available national data. Conclusions: Although many countries demonstrate that eliminating cervical cancer is achievable, global alignment with WHO targets remains uneven. Strengthening health infrastructure, improving vaccination uptake, and ensuring equitable access to screening and treatment are essential to turning this strategy into a realistic global outcome.

Background/Objectives: The use of artificial intelligence (AI) in oncology has the potential to improve decision making, particularly in managing the risk associated with targeted therapies. This study aimed to develop and validate a machine learning-based clinical decision support system (CDSS) capable of predicting complications associated with Bevacizumab or its biosimilars and to translate the resulting predictive model into a clinically applicable tool. Methods: A prospective observational study was conducted on 395 records from patients treated with Bevacizumab or biosimilars for solid tumors. Pretherapeutic variables, such as demographic data, medical history, tumor characteristics and laboratory findings, were retrieved from medical records. Several machine learning models (logistic regression, Random Forest, XGBoost) were trained using 70/30 and 80/20 data splits. Their predictive performances were compared using accuracy, AUC-ROC, sensitivity, specificity, F1-scores and error rate. The best-performing model was used to derive a logistic-based risk score, which was further implemented as an interactive HTML form. Results: The optimized Random Forest model trained on the 80/20 split demonstrated the best balance between accuracy (70.63%), sensitivity (66.67%), specificity (73.85%), and AUC-ROC (0.75). The derived logistic risk score showed good performance (AUC-ROC = 0.720) and calibration. It identified variables, such as age ≥ 65, anemia, elevated urea, leukocytosis, tumor differentiation, and stage, as significant predictors of complications. The final tool provides clinicians with an easy-to-use, offline form that estimates individual risk levels and stratifies patients into low-, intermediate-, or high-risk categories. Conclusions: This study offers a proof of concept for developing AI-supported predictive tools in oncology using real-world data. The resulting logistic risk score and interactive form can assist clinicians in tailoring therapeutic decisions for patients receiving targeted therapies, enhancing the personalization of care without replacing clinical judgment.

Background/Objective: Pelvic exenteration (PE) is a radical procedure with significant physical and psychosocial consequences. Despite increasing survival rate following PE, quality of life (QoL) outcomes remain inconsistently reported and poorly understood in clinical practice. This study aims to explore: (1) What is the current evidence on QoL after pelvic exenterations? and (2) How do patient-reported experiences align with or differ from findings in the literature? Methods: We conducted a mixed-methods study consisting of two components: (1) a qualitative analysis of 5 in-depth, semi-structured interviews with patients who underwent PE for advanced pelvic cancers; and (2) a narrative review of 28 quantitative and qualitative studies evaluating QoL after PE, published between 1975 and 2023, encompassing 1149 patients. Thematic analysis was performed using an interpretative phenomenological approach. Results: Qualitative findings revealed recurrent themes of identity disruption, social withdrawal, emotional resilience, and a need for personalized, preoperative information. Patients often described a mismatch between surgical expectations and lived experience, and expressed a strong desire for better psychological support and realistic communication. The review of published studies showed heterogeneous findings, with moderate recovery in global QoL scores by 6–12 months postoperatively, but persistent impairments in physical function, sexual health, and emotional well-being. Only a minority of studies included patient-reported outcomes tailored to specific domains such as body image or psychological adaptation (17.8%). Conclusions: Recovery after pelvic exenteration extends beyond physical healing and requires attention to emotional, social, and existential dimensions. Integrating psycho-oncologic support and patient-centered communication into standard care is essential. This hybrid analysis underscores the importance of addressing quality of life proactively—not only as an outcome, but as a fundamental component of survivorship care.

The “angiogenesis switch”—defined as the active process by which solid tumors develop their own circulation—plays an important role in both tumoral growth and propagation. As the malignant tumor grows and reaches a critical size, the metabolic needs as a function of an ever-increasing distance to the nearest emergent blood vessel, can no longer be covered by the microenvironment of the peritumoral tissue. Although a relatively discrete process, the “angiogenic switch” acts as a limiting stage of tumoral development present from the avascular hyperplasia phase to the vascularized neoplastic phase, providing support for tumor expansion and metastasis. Over time, research has focused on blocking the angiogenetic pathways (such as VEGF/VEGFR signaling axis) leading to the development of targeted therapeutic agents such as Bevacizumab. Objectives: We conducted a review of the molecular principles of tumoral angiogenesis and we tried to follow the history of Bevacizumab from its first approval for human usage 20 years ago to current days, focusing on the impact this agent had in solid tumor therapy. A comprehensive review of clinical trials pertaining to Bevacizumab (from the era of the preclinic trials leading to approval for human usage, to the more recent randomized trial focusing on combination targeted therapy) further details the role of this drug. We aimed to establish if this ancient drug continues to have a place in modern oncology. Conclusions: Bevacizumab, one of the first drugs targeting tumoral microenvironment, remains one of the most important oncologic agents blocking the VEGF/VEGFR angiogenic pathway. otherwise, history of 20 years marked by numerous controversies (ranging from methodological errors of clinical trials to withdrawal of approval for human usage in breast cancer patients, from discussions about severe side effects to resistance to therapy and limited efficacity), Bevacizumab continues to provide an optimal therapeutic option for many solid tumors that previously had little to no means of treatment, improving otherwise bleak outcomes. Even in the era of personalized precision oncology, Bevacizumab continues to be a key element in many therapeutic regimens both as monotherapy and in combination with newer targeted agents.

PURPOSE: Response assessment of classical Hodgkin lymphoma (cHL) with positron emission tomography-computerized tomography (PET-CT) is standard of care in well-resourced settings but unavailable in most African countries. We aimed to investigate correlations between changes in PET-CT findings at interim analysis with changes in blood test results in pediatric patients with cHL in 17 South African centers. METHODS: Changes in ferritin, lactate dehydrogenase (LDH), erythrocyte sedimentation rate (ESR), albumin, total white cell count (TWC), absolute lymphocyte count (ALC), and absolute eosinophil count were compared with PET-CT Deauville scores (DS) after two cycles of doxorubicin, bleomycin, vinblastine, and dacarbazine in 84 pediatric patients with cHL. DS 1-3 denoted rapid early response (RER) while DS 4-5 denoted slow early response (SER). Missing values were imputed using the k-nearest neighbor algorithm. Baseline and follow-up blood test values were combined into a single difference variable. Data were split into training and testing sets for analysis using Python scikit-learn 1.2.2 with logistic regression, random forests, naïve Bayes, and support vector machine classifiers. RESULTS: Random forest analysis achieved the best validated test accuracy of 73% when predicting RER or SER from blood samples. When applied to the full data set, the optimal model had a predictive accuracy of 80% and a receiver operating characteristic AUC of 89%. The most predictive variable was the differences in ALC, contributing 21% to the model. Differences in ferritin, LDH, and TWC contributed 15%-16%. Differences in ESR, hemoglobin, and albumin contributed 11%-12%. CONCLUSION: Changes in low-cost, widely available blood tests may predict chemosensitivity for pediatric cHL without access to PET-CT, identifying patients who may not require radiotherapy. Changes in these nonspecific blood tests should be assessed in combination with clinical findings and available imaging to avoid undertreatment.

PURPOSE: Cancers are a growing cause of mortality especially in low- and middle-income countries in Africa. Rwanda is no exception. Two cancer centers currently provide care to the public, but there are both political and human interest in expanding access to tertiary cancer care. Improved geographic access could lead to both better patient outcomes and a better understanding of the existing cancer burden across Rwanda. METHODS: To identify cost-aware ways of expanding geographic access, we adopt an optimization approach and identify expansion plans that minimize the average travel time to a cancer center across the country while remaining under a given monetary budget. RESULTS: Three additional hospitals could reduce average travel times by 40%, with the largest decrease in travel times observed in populations with long travel times. However, such an expansion would require a 50% increase in the number of in-country oncologists. We find that oncologist scarcity, as opposed to monetary constraints, is likely to be a limiting factor for improved access to cancer care. CONCLUSION: We present an array of expansion plans and suggest that further modeling approaches that incorporate oncologist scarcity can help deliver better policy recommendations.

The World Health Organization (WHO) recently launched the Global Initiative for Childhood Cancer (GICC), with the goal of attaining at least 60% cancer survival for children worldwide by the year 2030. This study aims to describe the global patterns of childhood cancer survival in 2019 to help guide progress in attaining the GICC target goal. In this ecological, cross-sectional study, we used 5-year net childhood cancer survival (2015–2019) data from a prior micro-modeling study from 197 countries and territories. Descriptive statistics were used to analyze the patterns of overall childhood cancer survival and survival for each of the six cancer tracer diagnoses as proposed by the GICC. We used hot spot analysis to identify geographic clusters of high and low cancer survival. Most high-income countries reached at least 60% (92%, n = 59/64), net childhood cancer survival at baseline. No lower-middle-income or low-income country reached at least 60% overall cancer survival at baseline. The South-East Asia region had the highest proportion of countries that did not achieve at least 60% survival at baseline (100%, n = 10/10), followed by the African region (98%, n = 49/50). For each cancer tracer diagnosis, we found the highest number of countries that have achieved at least 60% survival was for Burkitt lymphoma (44%, n = 87/197) followed by acute lymphocytic leukemia (41%, n = 80/197).Hot spot analysis showed the highest overall survival was concentrated in North America and Europe, while the lowest survival was concentrated in Sub-Saharan Africa and South-East Asia.A majority of LMICs had not reached the WHO target goal of at least 60% survival from childhood cancer at baseline in 2019, with variable success for the six childhood cancer tracer diagnoses of the GICC. These findings provide baseline assessment of individual country performance to help achieve the GICC goal of 60% overall cancer survival globally by 2030.