A Confidence Habitats Methodology in MR Quantitative Diffusion for the Classification of Neuroblastic Tumors

Characterisation of Paediatric Neuroblastic Tumours by Quantitative Structural and Diffusion-Weighted MRI

Purpose: To determine the diagnostic accuracy of quantitative diffusion-weighted (DW) MRI apparent diffusion coefficient (ADC) and tumour volumes to differentiate between malignant (neuroblastoma (NB)) and benign types of neuroblastic tumours (ganglioneuroma (GN) and ganglioneuroblastoma (GNB)) using different region-of-interest (ROI) sizes. Materials and Methods: This single-centre retrospective study included malignant and benign paediatric neuroblastic tumours that had undergone DW MRI at diagnosis. The outcome was diagnostic accuracy of the tumour volume from structural and ADC DW MRI, in comparison to histopathology (reference standard). Results: Data from 40 patients (NB, n = 24; GNB, n = 6; GN, n = 10), 18 (45%) females and 22 (55%) males, with a median age at diagnosis of 21 months (NB), 64 months (GNB), and 133 months (GN), respectively, ranging from 0 to 193 months, were evaluated. The area under the receiver operating characteristic (AUROC) curve for ADC for discriminating between neuroblastic tumours' histopathology for a small ROI was 0.86 (95% CI: 0.75-0.98), and for a large ROI, 0.83 (95% CI: 0.71-0.96). An ADC cut-off value of 1.06 × 10-3 mm2/s was able to distinguish malignant from benign tumours with 83% (68-98%) sensitivity and 75% (95% CI: 54-98%) specificity. Tumour volume was not indicative of malignant vs. benign tumour diagnosis. Conclusions: In this study, both small and large ROIs used to derive ADC DW MRI metrics demonstrated high accuracy to differentiate malignant from benign neuroblastic tumours, with the ADC AUROC for the averaged multiple small ROIs being slightly greater than that of large ROIs, but with overlapping 95% CIs. This should be taken into consideration for standardisation of ROI-related data analysis by international initiatives.

Imaging biomarkers and radiomics in pediatric oncology: a view from the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project

This review paper presents the practical development of imaging biomarkers in the scope of the PRIMAGE (PRedictive In silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) project, as a noninvasive and reliable way to improve the diagnosis and prognosis in pediatric oncology. The PRIMAGE project is a European multi-center research initiative that focuses on developing medical imaging-derived artificial intelligence (AI) solutions designed to enhance overall management and decision-making for two types of pediatric cancer: neuroblastoma and diffuse intrinsic pontine glioma. To allow this, the PRIMAGE project has created an open-cloud platform that combines imaging, clinical, and molecular data together with AI models developed from this data, creating a comprehensive decision support environment for clinicians managing patients with these two cancers. In order to achieve this, a standardized data processing and analysis workflow was implemented to generate robust and reliable predictions for different clinical endpoints. Magnetic resonance (MR) image harmonization and registration was performed as part of the workflow. Subsequently, an automated tool for the detection and segmentation of tumors was trained and internally validated. The Dice similarity coefficient obtained for the independent validation dataset was 0.997, indicating compatibility with the manual segmentation variability. Following this, radiomics and deep features were extracted and correlated with clinical endpoints. Finally, reproducible and relevant imaging quantitative features were integrated with clinical and molecular data to enrich both the predictive models and a set of visual analytics tools, making the PRIMAGE platform a complete clinical decision aid system. In order to ensure the advancement of research in this field and to foster engagement with the wider research community, the PRIMAGE data repository and platform are currently being integrated into the European Federation for Cancer Images (EUCAIM), which is the largest European cancer imaging research infrastructure created to date.

Pancreatic cancer, radiomics and artificial intelligence

Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.

Adrenal ganglioneuroma: Prognostic factors (Review)

Ganglioneuroma, a rare neural crest-derived tumor, exhibits a benign profile in contrast to other neuroblastic tumors (neuroblastoma/ganglioneuroblastoma). Ganglioneuromas can be found anywhere autonomic ganglia are located, mostly abdominal/pelvic sites followed by the adrenal glands (one-third of cases), mediastinum/thorax and cervical area. Affecting especially children more than 10 years of age, Ganglioneuroma is either asymptomatic or may cause local compressive effects; rarely inducing nonspecific abdominal complains or arterial hypertension related to oversecretion of epinephrine/norepinephrine/dopamine. Despite a good prognosis, adrenalectomy is necessary in order to rule out a malignancy. Open procedure represents the standard therapeutic option; alternatively, centers with large laparoscopic pediatric experience and good stratification protocols have reported successful procedures. High uptake of I¹²³-MIBG is associated with a more severe outcome in cases with increased mitotic index. In neuroblastic tumors, neuron-specific enolase >33 ng/ml, age at diagnosis <49 months, and blood vessel invasion indicate a poor prognosis. Concurrent extra-adrenal/adrenal ganglioneuroma is associated with a more severe prognosis; post-surgical complications are more frequent in non-adrenal vs. adrenal ganglioneuroma. Exceptionally, immune-mediated paraneoplastic neurologic syndromes have been reported: anti-N-methyl-D-aspartate receptor encephalitis and opsoclonus-myoclonus-ataxia syndrome. ROHHAD syndrome is the underlying cause in 40-56% of cases of neuroendocrine tumors including ganglioneuroma; 70% of tumors are diagnosed within the first 24 months after hypothalamic obesity onset, associated with a severe prognosis due to hypoventilation, sleep apnea, and dysautonomia. Recently, the PKB/AKT/mTOR/S6 pathway was identified as a tumorigenic pathway in pediatric ganglioneuroma, not in neuroblastoma; mTOR inhibitors are a potential option for pre-operatory tumor shrinkage. Pediatric adrenal ganglioneuroma has a good prognosis if adequately treated; its recognition requires adrenalectomy. Further development of specific biomarkers is needed. In the present article, we aimed to introduce a review of the literature involving adrenal ganglioneuroma based on a practical, multidisciplinary perspective of prognostic factors.