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PubMed2026

Surgical management and outcomes of total colonic aganglionosis in children: A systematic review and meta-analysis.

AIM: Total colonic aganglionosis (TCA) is a rare form of Hirschsprung disease, and there is no consensus regarding its optimal surgical management. This systematic review and meta-analysis aimed to evaluate different surgical approaches and outcomes in children with TCA. METHODS: A systematic search of PubMed/MEDLINE and Embase was performed for studies published between January 2000 and December 2025. The review followed PRISMA guidelines and was prospectively registered in PROSPERO (CRD420251078401). Eligible studies included patients aged ≤18 years with TCA who underwent conventional pull-through procedures (CPT; Duhamel, Soave, Swenson, Rehbein, and Ikeda-Soper) or non-conventional techniques (NCPT; STATE procedure, J-pouch, right- or left-sided colonic patch pull-through, and ileocecal patch). A subgroup analysis comparing Duhamel and ileoanal pull-through procedures (IAPT) was also performed. Outcomes included fecal incontinence, Hirschsprung-associated enterocolitis (HAEC), requirement for additional interventions, postoperative intestinal obstruction, and mortality. Meta-analysis was performed using jamovi software, version 2.3.28, with p < 0.05 considered statistically significant. RESULTS: Seven studies including 134 patients compared CPT (n = 85) with NCPT (n = 49), and ten studies including 274 patients compared Duhamel (n = 143) with IAPT (n = 131). Across both comparisons, pooled odds ratios (ORs) showed no statistically significant differences in fecal incontinence, HAEC, requirement for additional interventions, postoperative intestinal obstruction (Duhamel vs IAPT only), or mortality. For CPT versus NCPT, the pooled ORs were 1.1 for fecal incontinence (95% CI, 0.44-2.73; p = 0.837), 1.1 for HAEC (95% CI, 0.49-2.71; p = 0.743), 4.3 for requirement for additional interventions (95% CI, 0.86-22.1; p = 0.074), and 3.4 for mortality (95% CI, 0.52-21.5; p = 0.198). For Duhamel versus IAPT, the pooled ORs were 1.4 for fecal incontinence (95% CI, 0.60-3.36; p = 0.423), 0.6 for HAEC (95% CI, 0.22-2.06; p = 0.503), 1.8 for requirement for additional interventions (95% CI, 0.62-5.50; p = 0.262), 1.1 for postoperative intestinal obstruction (95% CI, 0.21-6.01; p = 0.875), and 1.03 for mortality (95% CI, 0.25-4.20; p = 0.965). CONCLUSION: No statistically significant differences were identified between CPT and NCPT or between Duhamel and IAPT for the evaluated outcomes in children with TCA. However, the absence of statistically significant differences should not be interpreted as evidence of equivalence, particularly given the small sample sizes, wide confidence intervals, and clinical and methodological heterogeneity of the studies included. The choice of surgical approach should be individualized according to disease extent, patient-specific factors, institutional experience, and surgical expertise. TYPE OF STUDY: Meta-analysis. LEVEL OF EVIDENCE: III.

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PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗

PubMed2026

Methods for defining equity-stratifying variables: a systematic review of validation studies.

BACKGROUND AND OBJECTIVE: Disease burden is often disproportionally higher among those who are socially disadvantaged by factors defined in the PROGRESS-Plus framework (ie, Place of residence, Race/ethnicity/culture/language, Occupation, Gender/sex, Religion, Education, Socioeconomic status, and Social capital, with "Plus" covering features like age and disability). The accuracy and applicability of case definitions to identify these variables from administrative and clinical health data are unknown. We conducted a systematic review to explore how equity-stratifying variables, as categorized by the PROGRESS-Plus framework, have been defined and validated in epidemiologic studies using administrative health, population-level, or electronic health record (EHR) data. METHODS: Medline, EMBASE, CINAHL, Web of Science, and Google Scholar were searched from the inception of the databases to 2024 for validation studies of equity-stratifying variables in adults using administrative health datasets, health registries, or EHR data. Titles and abstracts, followed by relevant full-text articles, were screened in duplicate by two reviewers for eligibility. The data sources utilized, algorithms employed, and their associated performance measures were extracted and synthesized from included studies. Given substantial heterogeneity in study design, equity-stratifying variable definition, and performance metrics, meta-analysis was not possible. RESULTS: Of the 9099 unique citations screened, 188 full texts were reviewed and 116 were included in this review. Most studies were published between 2019 and 2024 (n = 64, 55%) and were validation studies of race/ethnicity definitions that used race/ethnicity codes or surname list algorithms (n = 66, 57%). No studies examined religion. Regarding the reported performance measure estimates, the race/ethnicity/culture/language equity-stratifying variables category had the largest variability across sensitivity, positive predictive value (PPV), and Cohen's Kappa. Occupation validation studies had the lowest variation in sensitivity and PPV. CONCLUSION: Despite an increasing number of publications reporting on the validation of equity-stratifying variables relevant to the PROGRESS-Plus framework, performance measures varied widely across studies. The significant heterogeneity in equity-stratifying variable definitions and methods used to validate them support the need for further rigorous validation of equity-stratifying variables in administrative and clinical health data. PLAIN LANGUAGE SUMMARY: Disease burden is often higher in people who experience financial hardships, lower level of education, discrimination due to race/ethnicity, and unstable housing. These social factors can be considered health equity factors and are important for understanding health inequalities. Health researchers often use large datasets, such as hospital or electronic health records (EHRs), to study these health equity factors. However, it is not clear how accurately these data sources capture information about people's social circumstances and how these factors are defined. In this study, we reviewed existing research to understand how health equity factors have been defined across health data sources and how accurate they are at measuring aspects of health equity and social disadvantage. Of the more than 9000 studies we identified, we included 116 that met our criteria for this systematic review. Most included studies focused on identifying race and ethnicity, often using codes or surname-based methods. We found that the accuracy of these methods varied widely across studies, meaning results may not always be reliable or comparable. Overall, our findings show that there are inconsistencies in how social factors are defined and measured in health data. This makes it difficult to fully understand and address health inequalities using routinely collected health data. More work is needed to develop and validate better quality and more consistent methods for capturing these important social factors.

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