Rediscovering neuroblastoma pathology from the perspective of digital pathology and AI: Current challenges and future implications.
4 affiliations
- 1Pathology department, College of Medicine, RAK Medical and Health Sciences, Ras Al Khaimah, UAE.
- 2College of Medicine, Yarmouk University, Irbid, Jordan.
- 3Cellular Pathology Department, North Bristol NHS Trust, UK.
- 4Pathology department, Faculty of Medicine, Ain Shams University, Egypt.
Abstract
Neuroblastoma is the most common extracranial solid malignancy of childhood and demonstrates marked clinical and biologic heterogeneity. Histopathologic evaluation, particularly through the International Neuroblastoma Pathology Classification system, remains central to risk stratification by integrating patient age, degree of neuroblastic differentiation, Schwannian stromal development, and the mitosis-karyorrhexis index. However, diagnostic interpretation is challenged by intratumoral heterogeneity, sampling bias, interobserver variability, and the subjective assessment of subtle morphologic thresholds. These limitations directly influence prognostic categorization and subsequent clinical management.Digital pathology, through whole-slide imaging, provides a platform for standardized visualization and quantitative analysis of entire tumor sections. Recent computational and artificial intelligence-assisted approaches have demonstrated feasibility in tumor category classification, assessment of differentiation, automated mitosis-karyorrhexis index quantification, prognostic stratification, and prediction of molecular features such as MYCN amplification. While reported performance metrics are promising, most studies remain retrospective, single-institution studies, and limited by small pediatric datasets.Significant barriers to clinical translation persist, including the need for multi-institutional validation, workflow standardization, quality assurance frameworks, regulatory governance, and mitigation of algorithmic bias. Current evidence supports the role of digital and artificial intelligence tools as complementary aids rather than replacements for expert pathologist interpretation. Coordinated efforts addressing validation, infrastructure readiness, and ethical considerations will be essential for responsible integration of computational methods into neuroblastoma histopathology.
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BibTeX
@article{samman2026rediscovering,
title = {{Rediscovering neuroblastoma pathology from the perspective of digital pathology and AI: Current challenges and future implications.}},
author = {Lina M Samman and Osama Al-Samman and Mohamed Ahmed and Riham Ibrahim},
journal = {Pathologica},
year = {2026},
month = jun,
doi = {10.32074/1591-951x-2218},
url = {https://europepmc.org/article/MED/43012435},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Rediscovering neuroblastoma pathology from the perspective of digital pathology and AI: Current challenges and future implications.
AU - Lina M Samman
AU - Osama Al-Samman
AU - Mohamed Ahmed
AU - Riham Ibrahim
PY - 2026
DA - 2026/06/01
JO - Pathologica
DO - 10.32074/1591-951x-2218
UR - https://europepmc.org/article/MED/43012435
AB - Neuroblastoma is the most common extracranial solid malignancy of childhood and demonstrates marked clinical and biologic heterogeneity. Histopathologic evaluation, particularly through the International Neuroblastoma Pathology Classification system, remains central to risk stratification by integrating patient age, degree of neuroblastic differentiation, Schwannian stromal development, and the mitosis-karyorrhexis index. However, diagnostic interpretation is challenged by intratumoral heterogeneity, sampling bias, interobserver variability, and the subjective assessment of subtle morphologic thresholds. These limitations directly influence prognostic categorization and subsequent clinical management.Digital pathology, through whole-slide imaging, provides a platform for standardized visualization and quantitative analysis of entire tumor sections. Recent computational and artificial intelligence-assisted approaches have demonstrated feasibility in tumor category classification, assessment of differentiation, automated mitosis-karyorrhexis index quantification, prognostic stratification, and prediction of molecular features such as MYCN amplification. While reported performance metrics are promising, most studies remain retrospective, single-institution studies, and limited by small pediatric datasets.Significant barriers to clinical translation persist, including the need for multi-institutional validation, workflow standardization, quality assurance frameworks, regulatory governance, and mitigation of algorithmic bias. Current evidence supports the role of digital and artificial intelligence tools as complementary aids rather than replacements for expert pathologist interpretation. Coordinated efforts addressing validation, infrastructure readiness, and ethical considerations will be essential for responsible integration of computational methods into neuroblastoma histopathology.
SN - 0031-2983
SN - 1591-951X
ER -