Demystifying neuroblastoma malignancy through fractal dimension, entropy, and lacunarity

Abstract

Purpose: Neuroblastoma is a pediatric solid tumor with a prognosis associated with histology and age of the patient, which are the parameters of the well-established current classification (Shimada classification). Despite the development of new treatment options, the prognosis of high-risk neuroblastoma patients is still poor. Therefore, there is a continuous need to stratify the children suffering from this tumor. A mathematical and computational approach is proposed to enable automatic and precise cancer diagnosis on the histological slide. Methods: We targeted the complexity of neuroblastoma by calculating its image entropy ( S), fractal dimension (FD), and lacunarity (λ) in a combined mathematical code. First, we tested the proposed method for patient-derived glioma images. It allowed distinguishing between normal brain tissue, grade II, and grade III glioma, which harbor different outcomes. Results: In neuroblastoma, our analysis of image’s FD, S, and λ combined with a machine learning algorithm automatically predicted tumor malignancy with a receiver operating characteristic curve of 0.82. FD, S, and λ distinguish between neuroblastoma and ganglioneuroma, but they only partially differentiate between the normal samples and the other classes. Ganglioneuroma, the most differentiated form, and poorly-differentiated neuroblastoma display different values of FD, S, and λ. Conclusions: FD, S, and λ of imaging recognize groups in neuroblastic tumors. We suggest that future studies including these features may challenge the current Shimada classification of neuroblastoma with categories of favorable and unfavorable histology. It is expected that this methodology could trigger multicenter studies and potentially find practical use in the clinical setting of children’s hospitals worldwide.

In Situ Neuroblastoma: The Intriguing “Tumor” of Neuroectodermal Origin and the Putative Cancer Stem Cells

“…The bequest of Beckwith and Perrin' s classic contribution is a motivation for the future of young investigators to sit on the shoulders of giants. Here, creative thoughts may originate by re-reading old literature and implementing some ideas with new concepts originating from the most recent technological advancements as recently identified by Donato et al (51) , who are applying artificial intelligence for studying the neuroblastoma.…”

Imaging of Ganglioneuroma: A Literature Review and a Rare Case of Cystic Presentation in an Adolescent Girl

“…Concerning differential diagnosis, machine learning has been tested in a few studies to differentiate neuroblastoma from other tumors with a lower malignancy, like ganglioneuroma and ganglioneuroblastoma [ 52 , 53 , 54 ].…”

Exploring the mechanisms of kaempferol in neuroprotection: Implications for neurological disorders

“…While retinoic acid effectively induces differentiation and halts cell proliferation, resistance due to mutations in the RA receptor gene remains a challenge. Despite therapeutic advancements, neuroblastoma patients still confront a poor prognosis 126–130 …”

“…Despite therapeutic advancements, neuroblastoma patients still confront a poor prognosis. [126][127][128][129][130] Recent evidence indicates that kaempferol has the potential to promote neuroblastoma differentiation while reducing cell viability. 30 In a study comparing the effects of kaempferol and all-trans RA (ATRA) on neuroblastoma cell differentiation, the results demonstrated that cells treated with ATRA exhibited less morphological change in terms of neurite extensions compared to cells treated with kaempferol.…”