Introduction: Pediatric B non-Hodgkin lymphoma (B-NHL) often presents with complex karyotypes (CK), co-occurring high-risk mutations, and treatment resistance, leading to poor outcomes. Case description: We describe a child with a CK including MYC::IGH translocation, MYC inversion, TP53 deletion, BCL6 gain, and BCL2 loss, exhibiting a high tumor burden and a partial response to standard therapy, requiring treatment intensification. Conclusion: This case emphasizes the critical role of comprehensive cytogenetic and molecular profiling to guide diagnosis, risk stratification, and therapy selection. Recognition of high-risk genetic features can inform early intensification or targeted approaches. Pediatric MYC -rearranged B-NHL with complex cytogenetics remains under-characterized but clinically aggressive, underscoring the need for multicenter studies to define its genetic spectrum, refine prognostic models, and optimize individualized treatment strategies.
Background: The optimal follow-up strategy for head and neck squamous cell carcinoma (HNSCC) remains uncertain, with inconsistent international guidelines regarding routine radiological surveillance. Methods: A systematic search of PubMed/MEDLINE, EMBASE, and CENTRAL (to April 2025) identified studies including adults with HNSCC undergoing different follow-up modalities. Primary outcomes were overall (OS) and disease-free survival (DFS); secondary outcomes included recurrence detection mode, salvageability, and diagnostic accuracy. Results: Eighteen retrospective studies met inclusion criteria. Most recurrences (61.5%) were symptom-detected, followed by clinical (24%) and imaging (14.5%) surveillance. Salvageable recurrences were more frequent in symptom-based (60%) than clinical (45%) or imaging (30%) strategies. Across eight studies with survival data, no significant OS or DFS differences emerged between radiological and non-radiological strategies. Conclusion: Available evidence does not demonstrate a survival advantage associated with routine radiological surveillance after curative treatment for HNSCC. and may be limited to symptomatic cases, inaccessible sites, or early post-treatment assessment.
Objective: To evaluate the association between baseline metastatic tumor volume measured on computed tomography (CT) and survival outcomes in patients with metastatic renal cell carcinoma (mRCC) receiving nivolumab as second line or later line therapy. Materials and Methods: We retrospectively analyzed 43 patients with mRCC treated with nivolumab between August 2013 and March 2022. Baseline metastatic tumor volume was measured on CT scans obtained within 30 days before treatment initiation. The cohort median (117 cm 3 ) was used as an exploratory, data derived cut-off to define low- and high-volume groups. Progression free survival (PFS) and overall survival (OS) were evaluated using Kaplan–Meier analysis and Cox proportional hazards models. Results: Median PFS and OS were 26.97 and 28.11 months, respectively. Median PFS was 40.62 months in the low-volume group and 9.74 months in the high-volume group (p=0.012), while median OS was 46.9 and 12.89 months, respectively (p=0.006). High tumor volume and multiple metastatic sites were associated with poorer survival outcomes. Baseline imbalances in IMDC risk classification and nivolumab treatment line were observed between groups. Conclusion: Higher baseline metastatic tumor volume was associated with poorer survival outcomes in patients with mRCC treated with nivolumab. CT-based tumor volumetry may provide complementary prognostic information; however, these exploratory findings require validation in larger, prospective cohorts.
Cancer-related cognitive impairment (CRCI) is common in patients with non-central nervous system malignancies, but its identification remains difficult because symptoms are heterogeneous, may predate treatment, and are not captured by a single measure. This narrative review synthesizes evidence on epidemiology, clinical phenotype, risk factors, neuropsychological assessment, neuroimaging, and practical pathways for identifying CRCI in oncology care. Emerging literature highlights subjective-objective discordance and self-awareness as clinically relevant dimensions. Prevalence varies by timing and method, with higher estimates from self-report than from screening tools or formal batteries. Identification is more reliable when assessment combines patient-reported outcomes (PRO), brief objective screening, and focused neuropsychological testing within baseline vulnerability and concurrent symptom burden. Older age, lower cognitive reserve, pretreatment cognitive impairment, systemic illness, fatigue, insomnia, mood symptoms, pain, and medication effects are major modifiers. In clinical settings, neuroimaging is useful primarily for differential diagnosis: conventional brain MRI excludes competing neurologic disorders, while volumetric MRI, diffusion imaging, resting-state functional MRI, and [18F]FDG-PET currently provide group-level evidence rather than individual diagnostic signatures. At present, CRCI is best identified through a stepwise clinical strategy integrating PRO evaluation, objective testing, risk profiling, and selective imaging, considering individual patient modifiers rather than a single diagnostic test.
INTRODUCTION:This study aimed to develop radiomics-based multivariate classifiers able to non-invasively decipher the tumor immune microenvironment (TIME) in resected Non-small cell lung cancer (NSCLC) patients. METHODS:Tumor-infiltrating lymphocytes (TILs) subpopulations and PD-L1 TPS were immunohistochemically assessed. Radiomic features (RFs) were extracted from preoperative CT scans using the PyRadiomics platform. Six machine learning-based models were built to predict distinct TIME-related endpoints: PD-L1 (M1), CD8/CD3 ratio (M2), PD1/CD8 ratio (M3), CD3+ TILs (M4), CD4+ TILs (M5), CD8+ TILs (M6). Except for PD-L1 (classified as positive vs negative), patients were dichotomized into high versus low groups based on median values. Model development relied on Random Forest classifiers with sequential feature selection and internally validated through Leave-One-Out Cross-Validation splitsResults:A total of 98 patients with stage I-IIIA resected NSCLC were included. From 1702 initially extracted RFs per scan, 231 non-redundant features (116 tumor and 115 peritumoral) were retained after correlation filtering. Final models incorporated between 3 and 7 RFs derived from both tumor and peritumoral regions. Model performance showed accuracy, precision, sensitivity, receiver operating curve (ROC) Area under the Curve (AUC) and precision-recall curve (PRC) AUC values ranging from 70% to 80%, with slightly lower performance for M2. Specificity ranged from 55% to 77%, with the highest value observed in the CD8+ TILs model (M6). Feature selection varied across models, although one peritumoral feature (HLL-wavelet GLCM Correlation) was consistently selected in four models (M1, M2, M4, M5). CONCLUSIONS:Tumoral and peritumoral CT-based radiomics might effectively intercept TIME features, thus suggesting a potential non-invasive approach in resected NSCLC patients.
